
@book{Molenberghs_Verbeke_2005,
  author = {Geert Molenberghs and Geert Verbeke},
  title = {Models for Discrete Longitudinal Data},
  year = 2005,
  publisher = {Springer},
  address = {New York, U.S.A.}
}


@Manual{pkg:coxinterval,
  title = {\pkg{coxinterval}: Cox-Type Models for Interval-Censored Data},
  author = {Audrey Boruvka and Richard J. Cook},
  year = {2015},
  note = {\proglang{R} package version 1.2},
  url = {https://CRAN.R-project.org/package=coxinterval},
}

@Manual{pkg:MIICD,
  title = {\pkg{MIICD}: Multiple Imputation for Interval Censored Data},
  author = {Marc Delord},
  year = {2017},
  note = {\proglang{R} package version 2.4},
  url = {https://CRAN.R-project.org/package=MIICD},
}

@Manual{pkg:ICE,
  title = {\pkg{ICE}: Iterated Conditional Expectation},
  author = {W. John Braun},
  year = {2013},
  note = {\proglang{R} package version 0.69},
  url = {https://CRAN.R-project.org/package=ICE},
}

@article{Hothorn_2019_mtram,
  author = {Luisa Barbanti and Torsten Hothorn},
  title = {A Transformation Perspective on Marginal and Conditional Models},
  year = 2024,
  page = {kxac048},
  journal = {Biostatistics},
  volume = 25, 
  number = 2, 
  pages = {402--428},
  doi = {10.1093/biostatistics/kxac048},
}

@Article{	  matic_radoicic_2018,
  author	= {Ivan Mati{\'c} and Rado{\^s} Radoi{\^c}i{\'c} and Dan
		  Stefanica},
  title		= {A Sharp P{\'o}lya-Based Approximation to the Normal CDF},
  year		= 2018,
  journal	= {Applied Mathematics and Computation},
  volume	= 322,
  pages		= {111--122},
  doi           = {10.2139/ssrn.2842681}
}

@Article{	  heiss_winschel_2008,
  author	= {Florian Heiss and Viktor Winschel},
  title		= {Likelihood Approximation by Numerical Integration on
		  Sparse Grids},
  journal	= {Journal of Econometrics},
  volume	= 144,
  number	= 1,
  year		= 2008,
  pages		= {62--80},
  doi		= {10.1016/j.jeconom.2007.12.004}
}

@Article{	  pkg:lme4,
  title		= {Fitting Linear Mixed-Effects Models Using {lme4}},
  author	= {Douglas Bates and Martin M{\"a}chler and Ben Bolker and
		  Steve Walker},
  journal	= {Journal of Statistical Software},
  year		= {2015},
  volume	= {67},
  number	= {1},
  pages		= {1--48},
  doi		= {10.18637/jss.v067.i01}
}

@Article{         Pollet2009,
  author        = {Thomas V. Pollet and Daniel Nettle},
  title         = {Partner Wealth Predicts Self-Reported Orgasm Frequency in a Sample of Chinese Women},
  journal       = {Evolution and Human Behavior},
  year          = 2009,
  volume        = 30,  
  pages         = {146--151}
}

@Article{	  herberich+hothorn+nettle+pollet2010,
  author	= {Esther Herberich and Torsten Hothorn and Daniel Nettle and
		  Thomas V. Pollet},
  title		= {A Re-evaluation of the Statistical Model in {Pollet and
		  Nettle 2009}},
  journal	= {Evolution and Human Behavior},
  year		= 2010,
  volume	= {31},
  number	= 2,
  pages		= {150--151},
  code		= {http://cran.r-project.org/web/packages/multcomp/vignettes/chfls1.pdf}
}

@Article{	  roedel_graeven_fietkau_2015,
  author	= {Claus R\"odel and Ullrich Graeven and Rainer Fietkau and
		  Werner Hohenberger and Torsten Hothorn and Dirk Arnold and
		  Ralf-Dieter Hofheinz and Michael Ghadimi and Hendrik A.
		  Wolff and Marga Lang-Welzenbach and Hans-Rudolf Raab and
		  Christian Wittekind and Philipp Str\"obel and Ludger Staib
		  and Martin Wilhelm and Gerhard G. Grabenbauer and Hans
		  Hoffmanns and Fritz Lindemann and Anke Schlenska-Lange and
		  Gunnar Folprecht and Rolf Sauer and {Torsten Liersch on
		  behalf of the German Rectal Cancer Study Group}},
  title		= {Oxaliplatin Added to Fluorouracil-based Preoperative
		  Chemoradiotherapy and Postoperative Chemotherapy of Locally
		  Advanced Rectal Cancer (the {German} {CAO/ARO/AIO-04}
		  study): Final Results of the Multicentre, Open-label,
		  Randomised, Phase 3 Trial},
  journal	= {The Lancet Oncology},
  year		= 2015,
  doi		= {10.1016/S1470-2045(15)00159-X},
  volume	= 16,
  number	= 8,
  pages		= {979--989}
}

@Article{	  backer_vroey_1998,
  author	= {M. D. Backer and C. D. Vroey and E. Lesaffre and I. Scheys
		  and P. D. Keyser},
  title		= {Twelve Weeks of Continuous Oral Therapy for Toenail
		  Onychomycosis Caused by Dermatophytes: A Double-blind
		  Comparative Trial of Terbinafine 250 mg/day versus
		  Itraconazole 200 mg/day},
  journal	= {Journal of the American Academy of Dermatology},
  year		= 1998,
  volume	= 38,
  number	= {5(2)},
  pages		= {S57--S63},
  doi		= {10.1016/S0190-9622(98)70486-4}
}

@Article{	  chow_heller_2006,
  author	= {Roberta T. Chow and Gillian Z. Heller and Les Barnsley},
  title		= {The Effect of 300 {mW}, 830 nm Laser on Chronic Neck Pain:
		  A Double-blind, Randomized, Placebo-controlled Study},
  journal	= {Pain},
  year		= 2006,
  volume	= 124,
  number	= {1--2},
  pages		= {201--210},
  doi		= {10.1016/j.pain.2006.05.018}
}

@Article{	  ogden_2015,
  author	= {Helen E. Ogden},
  title		= {A Sequential Reduction Method for Inference in Generalized
		  Linear Mixed Models},
  journal	= {Electronic Journal of Statistics},
  year		= 2015,
  volume	= 9,
  pages		= {135--152},
  doi		= {10.1214/15-EJS991}
}

@Article{	  belenky_wesensten_thorne_2003,
  author	= {Gregory Belenky and Nancy J. Wesensten and David R. Thorne
		  and Maria L. Thomas and Helen C. Sing and Daniel P. Redmond
		  and Michael B. Russo and Thomas J. Balkin},
  year		= 2003,
  title		= {Patterns of Performance Degradation and Restoration During
		  Sleep Restriction and Subsequent Recovery: {A} Sleep
		  Dose-response Study},
  journal	= {Journal of Sleep Research},
  volume	= 12,
  pages		= {1--12},
  doi		= {10.1046/j.1365-2869.2003.00337.x}
}

@Article{	  manuguerra_heller_2010,
  author	= {Maurizio Manuguerra and Gillian Z. Heller},
  title		= {Ordinal Regression Models for Continuous Scales},
  journal	= {International Journal of Biostatistics},
  year		= 2010,
  volume	= 6,
  number	= 1,
  pages		= 14,
  doi		= {10.2202/1557-4679.1230}
}

@Article{	  boxcox_1964,
  author	= {G. E. P. Box and D. R. Cox},
  title		= {An Analysis of Transformations},
  volume	= {26},
  number	= {2},
  pages		= {211--252},
  year		= {1964},
  journal	= {Journal of the Royal Statistical Society: Series B
		  (Statistical Methodology)}
}


@techreport{Hothorn_Zeileis_2017,
  author = {Torsten Hothorn and Achim Zeileis},
  title = {Transformation Forests},
  year = {2017},
  institution = {arXiv 1701.02110, v2},
  url = {https://arxiv.org/abs/1701.02110}
}

@article{Lohse_Rohrmann_Faeh_2017,
  author = {Tina Lohse and Sabine Rohrmann and David Faeh and Torsten Hothorn},
  title = {Continuous Outcome Logistic Regression for Analyzing 
           Body Mass Index Distributions},
  journal = {F1000Research},
  year = 2017, 
  volume = 6,
  pages = {1933},
  doi = {10.12688/f1000research.12934.1}
}

@article{Korepanova_Seibold_Steffen_2019,
  author = {Natalia Korepanova and  Heidi Seibold and 
            Verena Steffen and Torsten Hothorn},
  title = {Survival Forests under Test: Impact of the 
           Proportional Hazards Assumption on
           Prognostic and Predictive Forests for ALS Survival},
  journal = {Statistical Methods in Medical Research},
  year = 2019,
  doi = {10.1177/0962280219862586},
}

@article{Hothorn_2019,
  author = {Torsten Hothorn},
  title = {Transformation Boosting Machines},
  year = 2019,
  journal = {Statistics and Computing},
  doi = {10.1007/s11222-019-09870-4}
}

@article{Hothorn_2018_SM,
  author = {Torsten Hothorn},
  title = {Top-Down Transformation Choice},
  year = 2018,
  journal = {Statistical Modelling},
  volume = 18,
  number = {3--4},
  pages = {274--298},
  doi = {10.1177/1471082X17748081}
}


@article{Hothorn_2018,
  author = {Torsten Hothorn},
  title = {Most Likely Transformations: The \textbf{mlt} Package},
  journal = {Journal of Statistical Software},
  year = 2020,
  doi = {10.18637/jss.v092.i01},
  volume = 92,
  number = 1,
  pages = {1--68}
}

@article{Seibold_Zeileis_Hothorn_2017,
  author = {Heidi Seibold and Achim Zeileis and Torsten Hothorn},
  title = {Individual Treatment Effect Prediction for {ALS} Patients},
  year = {2017},
  journal = {Statistical Methods in Medical Research},
  doi = {10.1177/0962280217693034},
}

@article{Seibold_Zeileis_Hothorn_2015,
  author = {Heidi Seibold and Achim Zeileis and Torsten Hothorn},
  title = {Model-Based Recursive Partitioning for Subgroup Analyses},
  journal = {International Journal of Biostatistics},
  year = 2016,
  doi = {10.1515/ijb-2015-0032},
  volume = 12,
  number = 1,
  pages = {45--63}
}

@Article{         Currie_Durban_Eilers_2006,
  author        = {I. D. Currie and M. Durban and P. H. C. Eilers},
  title         = {Generalized Linear Array Models With Applications to
                  Multidimensional Smoothing},
  year          = {2006},
  journal       = {Journal of the Royal Statistical Society B},
  volume        = {68},
  number        = {2}, 
  pages         = {259--280},
  doi           = {10.1111/j.1467-9868.2006.00543.x}
}

@book{vdGeer_2000,
  title = {Empirical Processes in M-Estimation},
  author = {Sarah van de Geer},
  year = 2000,
  publisher = {Cambridge University Press},
  address = {Cambridge, UK}
}

@book{vdVaart_1998,
  title = {Asymptotic Statistics},
  author = {Aart W. van der Vaart},
  year = 1998,
  publisher = {Cambridge University Press},
  address = {Cambridge, UK}
}

@book{Fraser_1968,
  title = {The Structure of Inference},
  author = {Donald A. S. Fraser},
  year = {1968},
  publisher = {John Wiley \& Sons},
  address = {New York, U.S.A.}
}

@article{Pitman_1939,
  title = {The Estimation of the Location and Scale Parameters
           of a Continuous Population of Any Given Form},
  author = {Edwin James George Pitman},
  journal = {Biometrika},
  volume = 30,
  number = {3/4},
  year = 1939,
  pages = {391--421}
}

@article{Fisher_1934,
  title = {Two New Properties of Mathematical Likelihood},
  author = {Ronald Aylmer Fisher},
  year = 1934,
  journal = {Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character},
  volume = 53,
  pages = {285--306}
}

@book{Lindsey_1996,
  title = {Parametric Statistical Inference},
  author = {Jim K. Lindsey},
  year = 1996,
  publisher = {Clarendon Press},
  address = {Oxford, UK}
}

@book{Tutz_2012,
  author = {Gerhard Tutz},
  year = 2012,
  title = {Regression for Categorical Data},
  publisher = {Cambridge University Press},
  address = {New York, U.S.A.},
}

@article{Hothorn_Kneib_Buehlmann_2014,
  author = {Torsten Hothorn and Thomas Kneib and Peter B{\"u}hlmann},
  title = {Conditional Transformation Models},
  journal = {Journal of the Royal Statistical Society B},
  year = {2014},
  volume = 76,
  number = 1,   
  doi = {10.1111/rssb.12017},
  pages = {3--27},
  code = {http://R-forge.R-project.org/projects/ctm},
  data = {http://R-forge.R-project.org/projects/ctm}
}

@Article{         Hayfield_Racine_2008,
  author        = {Tristen Hayfield and Jeffrey S. Racine},
  title         = {Nonparametric Econometrics: The np Package},
  journal       = {Journal of Statistical Software},
  volume        = {27},
  number        = {5},
  pages         = {1--32},
  day           = {29},
  month         = {7},
  year          = {2008},
  url           = {http://www.jstatsoft.org/v27/i05}
}

 
@Article{         Mayr_Fenske_Hofner_2012,  
  author        = {Andreas Mayr and Nora Fenske and Benjamin Hofner and
                  Thomas Kneib and Matthias Schmid},
  year          = 2012,
  title         = {{GAMLSS} for High-dimensional Data--{A} Flexible Approach
                  Based on Boosting},
  journal       = {Journal of the Royal Statistical Society: Series C
                  (Applied Statistics)},
  volume        = 61,
  number        = 3,
  pages         = {403--427},
  doi           = {10.1111/j.1467-9876.2011.01033.x}
}

@article{gbsg2:1994,
   author    = {M. Schumacher and G. Basert and H. Bojar and K. H\"ubner and
M. Olschewski and W. Sauerbrei and C. Schmoor and C. Beyerle and R. L. A.
Neumann and {Rauschecker, H. F., for the German Breast Cancer Study Group}},
   title     = {Randomized $2\times2$ Trial Evaluating Hormonal Treatment
and the Duration of Chemotherapy in Node-positive Breast Cancer Patients},
   journal   = {Journal of Clinical Oncology},
   year      = {1994},
   volume    = {12},
   pages     = {2086-2093}
}


@article{Hyndman_Yao_2002,
  author = {Rob J. Hyndman and Qiwei Yao},
  title = {Nonparametric Estimation and Symmetry Tests for Conditional Density Functions},
  year = {2002},
  journal = {Journal of Nonparametric Statistics},
  volume = {14},
  number = {3},
  DOI = {10.1080/10485250212374},
  pages = {259--278}
}

@article{Azzalini_Bowman_1990,
  author = {A. Azzalini and A. W. Bowman},
  title = {A Look at Some Data on the Old Faithful Geyser},
  year = {1990},
  journal = {Journal of the Royal Statistical Society: Series C (Applied Statistics)},
  volume = {39},
  number = {3},
  DOI = {10.2307/2347385},
  pages = {357--365}
}

@Article{Fredriks_Buuren_Burgmeijer_2000,
  author        = {A. M. Fredriks and S. van Buuren and R. J. F. Burgmeijer
                  and J. F. Meulmeester and R. J. Beuker and E. Brugman and
                  M. J. Roede and S. P. Verloove-Vanhorick and J. Wit},
  title         = {Continuing Positive Secular Growth Change in {The}
                  {Netherlands} 1955--1997},
  year          = {2000},
  journal       = {Pediatric Research},
  volume        = {47},
  number        = {3},
  pages         = {316--323}
}

@Article{         Stasinopoulos_Rigby_2007,
  author        = {D. Mikis Stasinopoulos and Robert A. Rigby},
  title         = {Generalized Additive Models for Location Scale and Shape
                  {(GAMLSS)} in {R}},
  year          = {2007},
  journal       = {Journal of Statistical Software},
  volume        = {23},
  number        = {7},
  pages         = {1--46},
  url           = {http://www.jstatsoft.org/v23/i07}
}

@Article{Mueller_Hothorn_2004,
   author    = {J{\"o}rg M{\"u}ller and Torsten Hothorn},
   title     = {Maximally Selected Two-Sample Statistics as a New Tool for
                the Identification and Assessment of Habitat Factors with
                an Application to Breeding Bird Communities in Oak Forests},
   year      = {2004}, 
   volume = 123,
   journal   = {European Journal of Forest Research},
   pages = {218--228},
   DOI = {10.1007/s10342-004-0035-5},
}

@book{Klein_Moeschberger_2003,
  author = {John P. Klein and Melvin K. Moeschberger},
  title = {Survival Analysis. Techniques for Censored and Truncated Data.},
  year = {2003},
  edition = {2nd},
  publisher = {Springer-Verlag},
  address = {New York, U.S.A.}
}

%VIP %LTM %spezi %splines %pen_lik
% NEW iterative optimisation algorithm: combination of Newton's method and an
% iterative multiplicative algorithm
@article{Maetal_2014,
  title = {On the Maximum Penalized Likelihood Approach for 
           Proportional Hazard Models with Right Censored Survival Data},
  journal = {Computational Statistics \& Data Analysis},
  volume = {74},
  pages = {142--156},
  year = {2014},
  doi = {10.1016/j.csda.2014.01.005},
  author = {J. Ma and S. Heritier and S. N. L\^{o}}
}



%VIP %LTM %flex_int_y %spezi %full_lik %splines
% note: right-censored log-likelihood is parametrised in terms of the hazard function
% => hazard function needs to be (numerically) integrated / computational disadvantage 
@article {CrowtherLambert_2014,
  author = {M. J. Crowther and P. C. Lambert},
  title = {A General Framework for Parametric Survival Analysis},
  journal = {Statistics in Medicine},
  volume = 33,
  number = 30,
  pages = {5280--5297},
  doi = {10.1002/sim.6300},
  year = {2014}
}


%VIP %flex_int %spezi %full_lik %splines
% Note: Parametrisations for conditional and unconditional log-hazard functions.
% PH models, PH models with time-varying effects, nonparametric PH models are included.
@article{Kooperbergetal_1995,
  title = {Hazard Regression},
  author = {Kooperberg, Charles and Stone, Charles J. and Truong, Young K.},
  journal = {Journal of the American Statistical Association},
  volume = {90},
  number = {429},
  pages = {78--94},
  year = {1995}
}

%optional %flex_int %spezi %full_lik %splines
% Note: Extension of Kooperberg et al. (1995) to interval-censored data and 
%       time-dependent covariates.
%       Modified Newton-Raphson algorithm because the log-likelihood is not 
%       necessarily concave for interval-censored data.
@article{KooperbergClarkson_1997,
  title = {Hazard Regression with Interval-Censored Data},
  author = {Kooperberg, Charles and Clarkson, Douglas B.},
  journal = {Biometrics},
  volume = {53},
  number = {4},
  pages = {1485--1494},
  year = {1997}
}


%LTM %spezi %full_lik %splines
% Note: Baseline-hazard function is approximated with quadratic splines.
@article{Etezadi-Amoli_Ciampi_1987,
  title = {Extended Hazard Regression for Censored Survival Data with 
           Covariates: A Spline Approximation for the Baseline Hazard Function},
  author = {Etezadi-Amoli, Jamshid and Ciampi, Antonio},
  journal = {Biometrics},
  volume = {43},
  number = {1},
  pages = {181--192},
  year = {1987}
}


%hazard_est %spezi %pen_lik %splines
% Note: Hazard function is parametrised in M-splines; I-splines are corresponding
%       splines for the cumulative hazard function; identical basis coefficients.
%       Approach can be extended to include covariate information.
%       Interval censored and interval truncated observations are considered.
@article{Jolyetal_1998,
  title = {A Penalized Likelihood Approach for Arbitrarily Censored 
           and Truncated Data: Application to Age-Specific Incidence of Dementia},
  author = {Joly, Pierre and Commenges, Daniel and Letenneur, Luc},
  journal = {Biometrics},
  volume = {54},
  number = {1},
  pages = {185--194},
  year = {1998}
}


%hazard_est %spezi %pen_lik %splines %optional
% Note: sqrt(lambda(y)) is parametrised; roughness penalty is considered.
%       General spline function that minimises the penalised log-likelihood
%       can be determined (similar principal as for natural cubic splines???)
@article{Senthil_1992,
  title = {Nonparametric Estimation of Hazard Function from Left 
           Truncated and Right Censored Data},
  author = {Selvan A. Senthil},
  journal = {Journal of Nonparametric Statistics},
  volume = {2},
  number = {1},
  pages = {29--35},
  year = {1992},
  doi = {10.1080/10485259208832540}
}


%hazard_est %spezi %pen_lik %splines %optional
% Note: estimation of an unconditional log-hazard function or log-density.
@article{Osullivan_1988,
  title = {Fast Computation of Fully Automated Log-Density and Log-Hazard Estimators},
  author = {Finbarr O'Sullivan},
  journal = {SIAM Journal on Scientific and Statistical Computing},
  volume = {9},
  number = {2},
  pages = {363--379},
  year = {1988}
}

%LTM %spezi %full_lik %splines 
% Note: baseline function has density with mild smoothness conditions; 
%       density is parametrised by a truncated series expantion with fixed degree of
%       truncation; adaptive choice of degree of truncation;
%       Given examples only include LTMs, but AFT-extensions to heteroscedastic errors
%       and time-dependent covariates are given in the appendix.
@article{ZhangDavidian_2008,
  author = {Zhang, Min and Davidian, Marie},
  title = {''Smooth'' Semiparametric Regression Analysis for 
           Arbitrarily Censored Time-to-Event Data},
  journal = {Biometrics},
  volume = {64},
  number = {2},
  doi = {10.1111/j.1541-0420.2007.00928.x},
  pages = {567--576},
  year = {2008}
}


%optional %LTM $spezi %full_lik %splines (= parametric transformation) %bayes
% Note: Transform both sides model is more restrictive than LTMs because both sides of
%       the model equation are transformed in terms of the same function g_lambda!
%       Extension of Box-Cox power family.
@article{Polpoetal_2014,
  title = {Transform Both Sides Model: A Parametric Approach},
  journal = {Computational Statistics \& Data Analysis},
  volume = {71},
  pages = {903--913},
  year = {2014},
  doi = {10.1016/j.csda.2013.07.023},
  author = {A. Polpo and C. P. {de Campos} and D. Sinha and S. Lipsitz and J. Lin}
}


%optional %LTM %spezi %full_lik %splines (= parametric transformation)
% Note: Generalisation of Box-Cox, e.g. to arbitrary but fixed error distribution F.
@article{BickelDoksum_1981,
  author = {P. J. Bickel and K. A. Doksum},
  title = {An Analysis of Transformations Revisited},
  journal = {Journal of the American Statistical Association},
  volume = {76},
  number = {374},
  pages = {296--311},
  year = {1981},
  doi = {10.1080/01621459.1981.10477649}
}

%optional
% Note: Reference for Exponential PH and Weibull PH model.
@article{Prentice_1973,
  author = {R. L. Prentice}, 
  title = {Exponential Survivals with Censoring and Explanatory Variables},
  volume = {60}, 
  number = {2}, 
  pages = {279--288}, 
  year = {1973}, 
  doi = {10.1093/biomet/60.2.279}, 
  journal = {Biometrika} 
}


%VIP %LTM %random %spezi %NPMLE %profile_lik or %full_lik
% Note: relationship of LTMs and frailty models;
%       time-dependent covariates are considered;
%       log-likelihood is formulated based on a counting process.
%       Model estimation: direct maximisation of full-lik or profile likelihood 
%       (both NPMLE).
@article {ChoiHuang_2012,
  author = {S. Choi and X. Huang},
  title = {A General Class of Semiparametric Transformation Frailty 
           Models for Nonproportional Hazards Survival Data},
  journal = {Biometrics},
  volume = {68},
  number = {4},
  doi = {10.1111/j.1541-0420.2012.01784.x},
  pages = {1126--1135},
  year = {2012}
}


%optional %LTM %profile_lik %spezi %NPMLE
% Note: log-likelihood based on counting processes is used;
%       time-dependent covariates are considered.
%       Estimation: weighted Breslow-type estimator for jump sizes of the cumulative
%       baseline function; profile score equation for regression coefficients.
%       Extension of Chen et al. (2002) (unweighted Breslow-type estimator)!
@article{Chen_2009,
  author = {Chen, Yi-Hau}, 
  title = {Weighted Breslow-type and Maximum Likelihood Estimation in 
           Semiparametric Transformation Models},
  year = {2009}, 
  volume = 96,
  number = 3,
  pages = {591--600},
  doi = {10.1093/biomet/asp032}, 
  journal = {Biometrika} 
}


%optional %spezi %NPMLE
% Note: proportional intensity model (for counting processes) with time-dependent
%       covariates;
%       nonparametric log-likelihood for counting processes;
%       LTM and PH model are included as special cases;
%       in contrast to Chen et al. (2002) the estimators are asympt. efficient;
%       quasi-Newton method for model estimation.
@article{ZengLin_2006,
  author = {D. Zeng and D. Y. Lin}, 
  title = {Efficient Estimation of Semiparametric Transformation 
           Models for Counting Processes},
  volume = {93}, 
  number = {3}, 
  pages = {627--640}, 
  year = {2006}, 
  doi = {10.1093/biomet/93.3.627}, 
  journal = {Biometrika} 
}


%VIP %flex_int %random %spezi %NPMLE
% Note: General class of transformation models for counting processes that is able to
%       handle time-dependent covariates, heteroscedasticity, multiple types of events,
%       random effects;
%       unified and general asymptotic theory is provided.
@article {ZengLin_2007,
  author = {D. Zeng and D. Y. Lin},
  title = {Maximum Likelihood Estimation in Semiparametric 
           Regression Models with Censored Data},
  journal = {Journal of the Royal Statistical Society B},
  volume = {69},
  number = {4},
  doi = {10.1111/j.1369-7412.2007.00606.x},
  pages = {507--564},
  year = {2007}
}


%optional %LTM %random %spezi %NPMLE
% Note: Aim: Prove large sample existence and consistency of the NPMLE.  
@article {SludVonta_2004,
  author = {E. V. Slud and F. Vonta},
  title = {Consistency of the {NPML} Estimator in the Right-Censored Transformation Model},
  journal = {Scandinavian Journal of Statistics},
  volume = {31},
  number = {1},
  doi = {10.1111/j.1467-9469.2004.00370.x},
  pages = {21--41},
  year = {2004}
}

%VIP %LTM_flex %spezi %NPMLE
% Note: Varying coefficients for covariates not for the response! 
%       Partial linear transformation model is included as a special case;
%       Varying coefficients are parametrised using B-splines;
%       Iterative procedure is suggested for model estimation because direct maximisation
%       of the log-likelihood is problematic because of the high number of parameters.
@article{ChenTong_2010,
  author = {K. Chen and X. Tong}, 
  title = {Varying Coefficient Transformation Models with Censored Data},
  volume = {97}, 
  number = {4}, 
  pages = {969--976}, 
  year = {2010}, 
  doi = {10.1093/biomet/asq032}, 
  journal = {Biometrika} 
}


%VIP %LTM (PO model) %spezi %NPMLE 
% Note: NPMLE approach for the PO model;
%       important reference for the PO model for survival data.
@article {Bennett_1983a,
  author = {Bennett, Steve},
  title = {Analysis of Survival Data by the Proportional Odds Model},
  journal = {Statistics in Medicine},
  volume = {2},
  number = {2},
  doi = {10.1002/sim.4780020223},
  pages = {273--277},
  year = {1983}
}


%LTM (PO model) %spezi %NPMLE
% Note: authors show that Bennett (1983)'s estimates are efficient;
%       large sample porperties of the estimators are established;
%       Newton-Raphson algorithm.
@article {Murphyetal_1997,
  author = {S. A. Murphy and A. J. Rossini and A. W. van der Vaart},
  title = {Maximum Likelihood Estimation in the Proportional Odds Model},
  journal = {Journal of the American Statistical Association},
  volume = {92},
  number = {439},
  doi = {10.1080/01621459.1997.10474051},
  pages = {968--976},
  year = {1997}
}

%optional %LTM (PO model) %spezi %NPMLE
% Note: identical approach to Murphy et al. (1997) but different estimation algorithm;
%       minorisation-maximisation algorithm;
%       Newton-Raphson might be problematic for large data sets where number of
%       parameters exceeds the number of uncensored observations. 
@article{HunterLange_2002,
  year={2002},
  journal={Annals of the Institute of Statistical Mathematics},
  volume={54},
  number={1},
  doi={10.1023/A:1016126007531},
  title={Computing Estimates in the Proportional Odds Model},
  author={D. R. Hunter and K. Lange},
  pages={155--168}
}


%flex_int_y (PO model with time-varying effects) %spezi %NPMLE %estequ
% Note: Extended PO model with time-varying coefficients or time-dependent covariates;
%       Estimation techniques:
%       1) Moment-based estimating equations (Chen et al. (2002))
%       2) NPMLE (Zeng and Lin (2006, 2007))
@article {Chenetal_2012,
  author = {Y. Q. Chen and N. Hu and S.-C. Cheng and P. Musoke and L. P. Zhao},
  title = {Estimating Regression Parameters in an Extended Proportional Odds Model},
  journal = {Journal of the American Statistical Association},
  volume = {107},
  number = {497},
  year = {2012},
  pages = {318--330},
  doi = {10.1080/01621459.2012.656021}
}

%optional %LTM %spezi %NPMLE
% Note: authors propose an efficient algorithm for calculating MLEs;
%       dimensionality of the parameter space is dramatically reduced.
@article{YinZeng_2006,
  title = {Efficient Algorithm for Computing Maximum Likelihood 
           Estimates in Linear Transformation Models},
  author = {G. Yin and D. Zeng},
  journal = {Journal of Computational and Graphical Statistics},
  volume = {15},
  number = {1},
  pages = {228--245},
  doi = {10.1198/106186006X100542},
  year = {2006}
}

%LTM (PO model) %random %spezi %NPMLE
% Note: right-censored log-likelihood is integrated over the random effects;
%       random effects follow a normal distribution.
@article {Zengetal_2005,
  author = {D. Zeng and D. Y. Lin and G. Yin},
  title = {Maximum Likelihood Estimation for the Proportional Odds Model with Random Effects},
  journal = {Journal of the American Statistical Association},
  volume = {100},
  number = {470},
  doi = {10.1198/016214504000001420},
  pages = {470--483},
  year = {2005}
}

%LTM_flex %random %spezi %NPMLE
% Note: NPMLE approach for linear transformation models with time-varying covariates
%       and random effects for clustered or correlated failure times.
@article{Zengetal_2008,
  author = {D. Zeng and D. Y. Lin and X. Lin},
  title = {Semiparametric Transformation Models with Random Effects 
           for Clustered Failure Time Data},
  journal = {Statistica Sinica},
  volume = {18},
  number = {1},
  pages = {355--377},
  year = {2008}
}


%LTM_flex %random (= frailty PH model) %spezi %NPMLE
% Note: NPMLE approach for semiparametric frailty Cox models;
%       frailty parameter is from a known one-parameter family of distributions with 
%       mean 1;
%       linear predictor with time-varying covariates.
@article{Kosoroketal_2004,
  title = {Robust Inference for Univariate Proportional Hazards Frailty Regression Models},
  author = {M. R. Kosorok and B. L. Lee and J. P. Fine},
  journal = {The Annals of Statistics},
  volume = {32},
  number = {4},
  pages = {1448--1491},
  doi = {10.1214/009053604000000535},
  year = {2004}
}


%VIP %LTM %random %spezi %NPMLE
% Note: NPMLE approach for the frailty PH model with arbitrarily truncated and 
%       censored response;
%       approach can be generalised to frailty LTMs by adapting link G.
@article{HuberCarolVonta_2004,
  year = {2004},
  journal = {Lifetime Data Analysis},
  volume = {10},
  number = {4},
  doi = {10.1007/s10985-004-4773-y},
  title = {Frailty Models for Arbitrarily Censored and Truncated Data},
  author = {C. Huber-Carol and I. Vonta},
  pages = {369--388}
}

%LTM_flex %spezi %NPMLE
% Note: LTM with nonparametric covariate effects (additive transformation model); 
%       nonparametric covariate effects are estimated using local linear regression.
@article{Zhaoetal_2007,
  author = {X. Zhao and X. Zhou and X. Wu},
  title = {Local Linear Regression in Proportional Hazards Model with Censored Data},
  journal = {Communications in Statistics--Theory and Methods},
  volume = {36},
  number = {15},
  pages = {2761--2776},
  year = {2007},
  doi = {10.1080/03610920701386828}
}


%VIP %LTM %random %pen_lik %mixedmod
% Note: log-hazard function is parametrised using a piecewise-linear spline;
%       penalised likelihood is maximised by a mixed model - based approach
%       => smooth estimate of the hazard function, where amount of smoothing is
%          estimated from the data;
%       integrated log-likelihood is approximated by a PQL approach; 
%       variance parameter of the spline coefficients / random effects is estimated
%       using REML.
@article{CaiBetensky_2003,
  author = {T. Cai and R. A. Betensky},
  title = {Hazard Regression for Interval-Censored Data with Penalized Spline},
  journal = {Biometrics},
  volume = {59},
  number = {3},
  doi = {10.1111/1541-0420.00067},
  pages = {570--579},
  year = {2003}
}



%LTM_flex (AFT model) %spezi %NPMLE %profile_lik
% Note: nonparametric ML method for AFT models with possibly time-dependent covariates;
%       regression parameters are estimated by maximising a kernel-smoothed profile
%       likelihood function;
%       cumulative hazard function has to be smoothed in AFT models because otherwise
%       there exists no maximum of the log-likelihood!
@article{ZengLin_2007a,
  author = {D. Zeng and D. Y. Lin}, 
  title = {Efficient Estimation for the Accelerated Failure Time Model},
  volume = {102}, 
  number = {480}, 
  pages = {1387--1396}, 
  year = {2007}, 
  doi = {10.1198/016214507000001085},
  journal = {Journal of the American Statistical Association} 
}

%VIP %LTM %not_spezi %estequ
% Note: dichotomous comparisons {I(Y_i > Y_j)} are used => h can be ignored because
%       rank configurations of Y_i > Y_j and h(Y_i) > h(Y_j) are identical;
%       estimating equation for beta which can be solved using Newton-Raphson algorithm;
%       right-censored observations: reweight estimating equation by the inverse
%       squared marginal Kaplan-Meier estimator (IPCW approach).
@article{Chengetal_1995,
  author = 	{S. C. Cheng and L. J. Wei and Z. Ying}, 
  title = 	{Analysis of Transformation Models with Censored Data},
  volume = 	{82}, 
  number = 	{4}, 
  pages = 	{835--845}, 
  year = 	{1995}, 
  doi = 	{10.1093/biomet/82.4.835}, 
  journal = 	{Biometrika} 
}

%VIP %LTM %spezi %estequ
% Note: authors define an additional estimating equation for the response transformation
%       h; this is necessary for the prediction of survival probabilities;
%       beta and h are estimated separately based on two estimating equations
%       => no simultaneous estimation of h and beta!
@article{Chengetal_1997,
  title = {Predicting Survival Probabilities with Semiparametric Transformation Models},
  author = {S. C. Cheng and L. J. Wei and Z. Ying},
  journal = {Journal of the American Statistical Association},
  volume = {92},
  number = {437},
  pages = {227--235},
  doi = {10.1080/01621459.1997.10473620},
  year = {1997}
}

%optional %LTM %spezi %estequ
% Note: Estimating equations of Cheng et al. (1995,1997) are simply modified because the 
%       estimators are asymptotically biased when the support of the censoring variable
%       is shorter than the support of the failure time;
%       root-finding equations are replaced by a least-squares criterion;
%       no simultaneous estimation of beta and h; 
%       => unique and consistent estimator
@article{Fineetal_1998,
  author = {J. P. Fine and Z. Ying and L. G. Wei},
  journal = {Biometrika}, 
  number = {4},
  pages = {980--986},
  title = {On the Linear Transformation Model for Censored Data},
  volume = {85},
  doi = {10.1093/biomet/85.4.980},
  year = {1998}
}

%optional %LTM_flex %spezi %estequ
% Note: linear transformation model is extended to parametric covariate transformations;
%       extension seems simple at first sight, but inference is challenging due to 
%       loss of identifiability under the hypotheses of no effects of transformed 
%       covariates.    
@article{FanFine_2013,
  author = {C. Fan and J. P. Fine},
  title = {Linear Transformation Model with Parametric Covariate Transformations},
  journal = {Journal of the American Statistical Association},
  volume = {108},
  number = {502},
  pages = {701--712},
  year = {2013},
  doi = {10.1080/01621459.2013.770707}
}


%optional %LTM_flex %spezi %estequ
% Note: LTMs are extended to correlated observations;
%       introduction of subject- and cluster-specific time-dependent covariates:
%       h(T_ik) = - beta^T %*% Z_ik(t) + eps;
%       estimating equations for beta and h (GEE with indpendence working assumption);
%       approach is more similar to NPMLE than to alternative estimating equations.
@article{Caietal_2000,
  author = {T. Cai and L. J. Wei and M. Wilcox},
  journal = {Biometrika},
  number = {4},
  pages = {867--878},
  title = {Semiparametric Regression Analysis for Clustered Failure Time Data},
  volume = {87},
  doi = {10.1093/biomet/87.4.867},
  year = {2000}
}

%VIP %LTM %random %spezi %estequ
% Note: Linear transformation model with random effects; 
%       frailty Cox model is included as a special case;
%       distribution of the random effects is specified up to a parameter;
%       h and beta are not estimated simultaneously but using separate estimating
%       equations.
@article{Caietal_2002,
  author = {T. Cai and S. C. Cheng and L. J. Wei},
  title = {Semiparametric Mixed-Effects Models for Clustered Failure Time Data},
  journal = {Journal of the American Statistical Association},
  volume = {97},
  number = {458},
  pages = {514--522},
  year = {2002},
  doi = {10.1198/016214502760047041}
}


%optional %LTM %not_spezi %estequ
% Note: LTMs for interval-censored observations;
%       rank-based estimating equation for the regression coefficients is provided
%       => baseline-free approach; 
%       approach is restricted to discrete covariates!
%% no DOI (checked)
@article{Zhangetal_2005,
  title = {Regression Analysis of Interval-Censored Failure Time Data 
           with Linear Transformation Models},
  author = {Z. Zhang and L. Sun and X. Zhao and J. Sun},
  journal = {The Canadian Journal of Statistics},
  volume = {33},
  number = {1},
  pages = {61--70},
  year = {2005}
}

%optional %LTM %spezi %estequ
% Note: approach of Zhang et al. (2005) is extended to survival probability prediction
%       by defining a separate estimating equation for h;
%       beta and h are not estimated simultaneously in the presence of interval
%       censoring;
%       tools for model checking and model selection are additionally considered.
@article{Zhang_2009,
  author = {Z. Zhang}, 
  title = {Linear Transformation Models for Interval-Censored Data: 
           Prediction of Survival Probability and Model Checking},
  volume = {9}, 
  number = {4}, 
  pages = {321--343}, 
  year = {2009}, 
  doi = {10.1177/1471082X0900900404},  
  journal = {Statistical Modelling} 
}


%LTM %random %not_spezi %estequ
% Note: consideration of a panel data version of the LTM with random effects in 
%       ECONOMETRICS!
%       estimating equation only for the regression coefficients beta
%       => baseline-free approach
@article{Lee_2008,
  author = {S. Lee},
  title = {Estimating Panel Data Duration Models with Censored Data},
  journal = {Econometric Theory},
  volume = {24},
  number = {05},
  year = {2008},
  pages = {1254--1276},
  doi = {10.1017/S0266466608080493}
}


%VIP %LTM %spezi %estequ
% Note: most estimating equations (e.g. Cheng et al. (1995)) assume independence of 
%       the censoring time C and the covariates X because the marginal Kaplan-Meier
%       estimator of the censoring distribution is used in the IPCWs;
%       assumption is relaxed here + e.g. Cox model does not make this assumption
%       => Cox partial likelihood estimator results as a special case;
%       maringale-based estimating equations for beta and h => beta and h are 
%       estimated iteratively;
%       h is assumed to be a nondecreasing step function with jumps at the failure 
%       times. 
@article{Chenetal_2002,
  author = {K. Chen and Z. Jin and Z. Ying}, 
  title = {Semiparametric Analysis of Transformation Models with Censored Data},
  volume = {89}, 
  number = {3}, 
  pages = {659--668}, 
  year = {2002}, 
  doi = {10.1093/biomet/89.3.659}, 
  journal = {Biometrika} 
}


%LTM_flex %spezi %estequ
% Note: non-linear covariate effects are incorporated into the LTM
%       => partially linear transformation model;
%       global and kernel-weighted local estimating equations based on martingale theory
%       => extension of Chen et al. (2002)'s EEs to non-linear covariate effects.
%       Global EEs for beta and h (step function) similar to Chen et al. (2002);
%       Local kernel-weighted EE for nonlinear covariate effects given beta and h.
@article{LuZhang_2010,
  author = {W. Lu and H. H. Zhang},
  title = {On Estimation of Partially Linear Transformation Models},
  journal = {Journal of the American Statistical Association},
  volume = {105},
  number = {490},
  pages = {683--691},
  year = {2010},
  doi = {10.1198/jasa.2010.tm09302}
}

%VIP %flex_int_y %spezi %estequ
% Note: LTMs are extended to time-varying structure for the transformation function
%       => time-varying structure in the data is allowed, baseline function may 
%          depend on the covariates;
%       stratified LTM is included as a special case;
%       martingale estimating equations are defined for model estimation.
@article{Scheike_2006,
  year = {2006},
  journal = {Lifetime Data Analysis},
  volume = {12},
  number = {4},
  doi = {10.1007/s10985-006-9021-1},
  title = {A Flexible Semiparametric Transformation Model for Survival Data},
  author = {T. H. Scheike},
  pages = {461--480}
}



%LTM %not_spezi %partial_lik
k@article{Cox_1972,
  author = {D. R. Cox},
  title = {Regression Models and Life-Tables},
  journal = {Journal of the Royal Statistical Society B},
  volume = {34},
  number = {2},
  pages = {187--220},
  year = {1972}
}

%LTM %not_spezi %partial_lik
@article{Cox_1975,
  author = {D. R. Cox},
  title = {Partial Likelihood},
  journal = {Biometrika},
  volume = {62},
  number = {2},
  pages = {269--276},
  year = {1975}
}

%VIP %flex_int_y %not_spezi %partial_lik
% Note: Cox model with time-dependent coefficients;
%       estimation via local partial likelihood techniques;
%       local linear fitting techniques known from scatterplot smoothing are used;
%       coefficients are estimated locally based on the partial likelihood in a 
%       window around each time point;
%       approach can be used to check the validity of the PH assumption.
@article {CaiSun_2003,
  author = {Z. Cai and Y. Sun},
  title = {Local Linear Estimation for Time-Dependent 
           Coefficients in {C}ox's Regression Models},
  journal = {Scandinavian Journal of Statistics},
  volume = {30},
  number = {1},
  doi = {10.1111/1467-9469.00320},
  pages = {93--111},
  year = {2003}
}


%LTM_flex %not_spezi %partial_lik
@article{SlepperHarrington_1990,
  author = {Sleeper, Lynn A. and Harrington, David P.},
  title = {Regression Splines in the {C}ox Model with Application 
           to Covariate Effects in Liver Disease},
  journal = {Journal of the American Statistical Association},
  volume = {85},
  number = {412},
  pages = {941--949},
  year = {1990},
  doi = {10.1080/01621459.1990.10474965}
}


%flex_int %not_spezi %marg_lik
% Note: baseline-free approach for a general class of semiparametric transformation
%       models under interval censoring;
%       model estimation via maximising the marginal log-likelihood;
%       marginal log-likelihood has no closed analytic form => three-stage MCMC
%       stochastic approximation algorithm;
%       frailty models and heteroscedastic hazard/rank regression models are included
%       as special cases.
@article{Guetal_2005,
  year = {2005},
  journal = {Lifetime Data Analysis},
  volume = {11},
  number = {4},
  doi = {10.1007/s10985-005-5235-x},
  title = {A Baseline-free Procedure for Transformation Models Under Interval Censorship},
  author={M. Gu and L. Sun and G. Zuo},
  pages = {473--488}
}

%LTM (= PO model) %not_spezi %marg_lik
% Note: survival times are replaced by their ranks => marginal likelihood is maximised
%       w.r.t. the regression coefficients.
@article {Pettitt_1984,
  author = {A. N. Pettitt},
  title = {Proportional Odds Models for Survival Data and Estimates Using Ranks},
  journal = {Journal of the Royal Statistical Society: Series C (Applied Statistics)},
  volume = {33},
  number = 2,
  pages = {169--175},
  year = {1984}
}


%LTM (= PO model) %not_spezi %marg_lik
% Note: baseline-free approach because only rank order information among patients is
%       used; rank order preserves information about the regression parameters;
%       marginal likelihood is approximated using a Monte Carlo method.
@article{LamLeung_2001,
  year = {2001},
  journal = {Lifetime Data Analysis},
  volume = {7},
  number = {1}, 
  doi = {10.1023/A:1009673026121},
  title = {Marginal Likelihood Estimation for Proportional Odds Models 
           with Right Censored Data},
  author = {K. F. Lam and T. L. Leung},
  pages = {39--54}
}


%LTM (= PO model) %random %not_spezi %marg_lik
% Note: Semiparametric random effects PO model for multivariate survival times with 
%       various types of dependence structures;
%       cluster data and repeated measurements are covered;
%       assumption of multivariate normal effects;
%       regression and variance parameters are estimated by maximising the 
%       marginal rank likelihood;
%       marginal likelihood is approximated by a Monte Carlo method.
@article{Lametal_2002,
  author = {K. F. Lam and Y. W. Lee and T. L. Leung},
  title = {Modeling Multivariate Survival Data by a Semiparametric 
           Random Effects Proportional Odds Model},
  journal = {Biometrics},
  volume = {58},
  number = {2},
  doi = {10.1111/j.0006-341X.2002.00316.x},
  pages = {316--323},
  year = {2002}
}


%LTM_flex %not_spezi %marg_lik
% Note: special case of Gu et al. (2005);
%       varying coefficient component is included in linear transformation models;
%       coefficient functions are parametrised using B-splines;
%       estimation is based on maximising the marginal rank log-likelihood;
%       three-stage MCMC stochastic approximation algorithm.
@article{Lietal_2012,
  author = {J. Li and M. Gu and T. Hu},
  title = {General Partially Linear Varying-Coefficient 
           Transformation Models for Ranking Data},
  journal = {Journal of Applied Statistics},
  volume = {39},
  number = {7},
  pages = {1475--1488},
  year = {2012},
  doi = {10.1080/02664763.2012.658357}
}

%VIP %LTM %spezi %bayes
% Note: Bayesian semiparametric transformation model;
%       PH, PH and AFT are included as special cases;
%       can be generalised to multivariate survival data using frailty models;
%       Priors: 
%       beta: multivariate normal
%       h: mixture of incomplete beta functions
%       F: Polya tree prior;
%       MCMC algorithm for model estimation.
@article{MallickWalker_2003,
  title = {A {B}ayesian Semiparametric Transformation Model Incorporating Frailties},
  journal = {Journal of Statistical Planning and Inference},
  volume = {112},
  number = {1--2},
  pages = {159--174},
  year = {2003},
  doi = {10.1016/S0378-3758(02)00330-0},
  author = {B. K. Mallick and S. Walker}
}


%LTM (= PO model) %spezi %bayes
% Note: Bayesian semiparametric PO model;
%       prior for the baseline survival function: mixture of finite Polya trees.
@article{HansonYang_2007,
  author = {T. Hanson and M. Yang},
  title = {Bayesian Semiparametric Proportional Odds Models},
  journal = {Biometrics},
  volume = {63},
  number = {1},
  doi = {10.1111/j.1541-0420.2006.00671.x},
  pages = {88--95},
  year = {2007}
}

%flex_int %spezi %bayes
% Note: Bayesian analysis of a class of generalised odds-rate hazards (GORH) models;
%       non-PH, PH, PO, AFT are included as special cases.
@article{Banerjeeetal_2007, 
  year = {2007},
  journal = {Lifetime Data Analysis},
  volume = {13},
  number = {2},
  doi = {10.1007/s10985-007-9035-3},
  title = {Bayesian Analysis of Generalized Odds-Rate Hazards Models for Survival Data},
  author = {T. Banerjee and M.-H Chen and D. K. Dey and S. Kim},
  pages = {241--260}
}


%further_extension %LTM %spezi %estequ
% Note: authors suggest a Box-Cox transformation of the survivor function;
%       model class is related to frailty models.
@article {ZuckerYang_2006,
  author = {D. M. Zucker and S. Yang},
  title = {Inference for a Family of Survival Models Encompassing 
           the Proportional Hazards and Proportional Odds Models},
  journal = {Statistics in Medicine},
  volume = {25},
  number = {6},
  doi = {10.1002/sim.2255},
  pages = {995--1014},
  year = {2006}
}

%further_extension %LTM %spezi %profile_lik
% Note: response transformation h is parametric, but F and covariate effects are 
%       nonparametric;
%       constraints: h, F and covariate effects are smooth; h and F monotone increasing.
@article{lintonetal_2008,
  author = {O. Linton and S. Sperlich and I. {van Keilegom}},
  doi = {10.1214/009053607000000848},
  journal = {The Annals of Statistics},
  number = {2},
  pages = {686--718},
  title = {Estimation of a Semiparametric Transformation Model},
  volume = {36},
  year = {2008}
}


%further_extension %LTM %spezi %marg_lik
% Note: h and F are unspecified; 
%       smoothed partial rank estimator is suggested;
@article{Songetal_2007,
  author = 	{X. Song and S. Ma and J. Huang and X.-H. Zhou}, 
  title = 	{A Semiparametric Approach for the Nonparametric 
                 Transformation Survival Model with Multiple Covariates},
  volume = 	{8}, 
  number = 	{2}, 
  pages = 	{197--211}, 
  year = 	{2007}, 
  doi = 	{10.1093/biostatistics/kxl001}, 
  journal = 	{Biostatistics} 
}


%VIP %furhter_extension
% Note: Transformation models in econometrics;
%       - h and F are parametric => maximum likelihood methods;
%       - h is parametric, F is nonparmetric => F is estimated based on the empirical 
%         distribution of the residuals; beta with the generalised method of moments
%       - h and F are nonparametric => estimation techniques for single index models
@book{Horowitz_2009,
  author = {J. L. Horowitz},
  title = {Semiparametric and Nonparametric Methods in Econometrics},
  publisher = {Springer-Verlag},
  year = {2009},
  address = {New York, U.S.A.}
}


% Semiparametric AFT models (i.e. F is nonparametric):
@article{Jinetal_2003,
  author = {Z. Jin and D. Y. Lin and L. J. Wei and Z. Ying}, 
  title = {Rank--based Inference for the Accelerated Failure Time Model},
  volume = {90}, 
  number = {2}, 
  pages = {341--353}, 
  year = {2003}, 
  doi = {10.1093/biomet/90.2.341}, 
  journal = {Biometrika} 
}

@Manual{R,
  title = {R: A Language and Environment for Statistical Computing},
  author = {{R Core Team}},
  organization = {R Foundation for Statistical Computing},
  address = {Vienna, Austria},
  year = {2015},
  url = {http://www.R-project.org/},
}

@article{Horowitz_1996,
  author = {Joel L. Horowitz},
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  number = 1,
  year = 1996,
  pages = {103--137}
}

@article{Jones_Pewsey_2009,
  author = {M. C. Jones and A. Pewsey},
  title = {Sinh-arcsinh Distributions},
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  journal = {Biometrika},
  volume = {96},
  number = {4},
  DOI = {10.1093/biomet/asp053},
  pages = {761--780}
}

@article{Bove_Held_2013,
  author = {Daniel {Saban{\'e}s Bov{\'e}} and Leonhard Held},
  title = {Comment on {Cai} and {Betensky} (2003), {On} the {Poisson} Approximation 
           for Hazard Regression},
  year = {2013},
  journal = {Biometrics},
  volume = {69},
  number = {3},
  DOI = {10.1111/biom.12088},
  pages = {795--795}
}

@article{Chang_Hsiung_Wu_2005,
  author = {I. Chang and Chao A. Hsiung and Yuh-Jenn Wu and Che-Chi Yany},
  title = {Bayesian Survival Analysis Using {Bernstein} Polynomials},
  year = {2005},
  journal = {Scandinavian Journal of Statistics},
  volume = {32},
  number = {3},
  pages = {447--466},
  doi = {10.1111/j.1467-9469.2005.00451.x}
}

@article{Curtis_Ghosh_2011,
  author = {S. McKay Curtis and Sujit K. Ghosh},
  title = {A Variable Selection Approach to Monotonic Regression 
           with {Bernstein} Polynomials},
  year = {2011},
  journal = {Journal of Applied Statistics},
  volume = {38},
  number = {5},
  DOI = {10.1080/02664761003692423},
  pages = {961--976}
}

@article{Farouki_2012,
  author = {Rida T. Farouki},
  title = {The {Bernstein} Polynomial Basis: A Centennial Retrospective},
  year = {2012},
  journal = {Computer Aided Geometric Design},
  volume = {29},
  number = {6},
  DOI = {10.1016/j.cagd.2012.03.001},
  pages = {379--419}
}

@article{Sun_Sundaram_Zhao_2009,
  author = {Yanqing Sun and Rajeshwari Sundaram and Yichuan Zhao},
  title = {Empirical Likelihood Inference for the {Cox} Model with Time-dependent
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  year = {2009},
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  number = {3},
  DOI = {10.1111/j.1467-9469.2008.00634.x},
  pages = {444--462}
}

@Article{Hothorn_Bretz_Westfall_2008,
  author = {Torsten Hothorn and Frank Bretz and Peter Westfall},  
  year = 2008,
  title = {Simultaneous Inference in General Parametric Models},
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  volume = 50,
  number = 3,
  pages = {346--363},
  doi = {10.1002/bimj.200810425},
}

@article{Royston_Parmar_2002,
  author = {Patrick Royston and Mahesh K. B. Parmar},
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  year = {2002},
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  volume = {21},
  number = {15},
  DOI = {10.1002/sim.1203},
  pages = {2175--2197}
}

@Book{            Koenker_2005,
  title         = {Quantile Regression},
  publisher     = {Cambridge University Press},
  year          = {2005},
  author        = {Roger Koenker},
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  language      = {english},
  address       = {New York, U.S.A.}
}

@Article{Varadhan_Gilbert_2009,
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}

@article{Chernozhukov_2013,
        author = {Victor Chernozhukov and Iv{\'a}n Fern{\'a}ndez-Val and Blaise Melly},        
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	number = {6},
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	year = {2013},
	pages = {2205--2268}
}

@article{Foresi_Peracchi_1995,
  author = {Silverio Foresi and Franco Peracchi},
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@techreport{Koenker_Leorato_Peracchi_2013,
  author = {Roger Koenker and Samantha Leorato and Franco Peracchi},
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  year = {2013},
  doi = {10.2139/ssrn.2368737},
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  author = {Christoph Rothe and Dominik Wied},
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  volume = {74},
  number = {4},
  DOI = {10.1111/j.1467-9868.2011.01020.x},
  pages = {623--671}
}

@article{Rigby_Stasinopoulos_2004,
  author = {Robert A. Rigby and D. Mikis Stasinopoulos},
  title = {Smooth Centile Curves for Skew and Kurtotic Data Modelled Using the {Box-Cox} Power Exponential Distribution},
  year = {2004},
  journal = {Statistics in Medicine},
  volume = {23},
  number = {19},
  DOI = {10.1002/sim.1861},
  pages = {3053--3076}
}


@Article{         Rigby_Stasinopoulos_2005,
  author        = {R. A. Rigby and D. M. Stasinopoulos},
  title         = {Generalized Additive Models for Location, Scale and
                  Shape},
  year          = {2005},
  journal       = {Journal of the Royal Statistical Society: Series C
                  (Applied Statistics)},
  volume        = {54},
  number        = {3},
  pages         = {507--554},
  doi           = {10.1111/j.1467-9876.2005.00510.x}
}

@Article{         Doksum_Gasko_1990,
  author        = {Kjell A. Doksum and Miriam Gasko},   
  title         = {On a Correspondence Between Models in Binary Regression
                  Analysis and in Survival Analysis},
  year          = {1990},
  journal       = {International Statistical Review},
  volume        = {58},
  number        = {3},
  pages         = {243--252}
}

@article{Gneiting_Katzfuss_2014,
  author = {Tilmann Gneiting and Matthias Katzfuss},
  title = {Probabilistic Forecasting},
  year = {2014},
  journal = {Annual Review of Statistics and Its Application},
  volume = {1},
  number = {1},
  DOI = {10.1146/annurev-statistics-062713-085831},
  pages = {125--151}
}

### constant effects of time-varying covariates
@article{Ding_Tian_Yu_2012,
  author = {A. Adam Ding and Shaonan Tian and Yan Yu and Hui Guo},
  title = {A Class of Discrete Transformation Survival Models With Application
           to Default Probability Prediction},
  year = {2012},
  journal = {Journal of the American Statistical Association},
  volume = {107},
  number = {499},
  DOI = {10.1080/01621459.2012.682806},
  pages = {990--1003}
}

@article{Feng_Sun_2014,
  author = {Yanqin Feng and Jianguo Sun},
  title = {Hazard Ratio Estimation for Two-sample Case Under Interval-censored Failure Time Data},
  year = {2014},
  journal = {Biometrical Journal},
  volume = {56},
  number = {2},
  DOI = {10.1002/bimj.201200273},
  pages = {219--229}
}

@article{Huang_2014,
  author = {Alan Huang},
  title = {Joint Estimation of the Mean and Error Distribution in 
           Generalized Linear Models},
  year = {2014},
  journal = {Journal of the American Statistical Association},
  volume = {109},
  number = {505},
  DOI = {10.1080/01621459.2013.824892},
  pages = {186--196}
}

@article{Lindsey_1999,
  author = {Jim K. Lindsey},
  title = {Some Statistical Heresies},
  year = 1999,
  journal = {Journal of the Royal Statistical Society: Series D (The Statistician)},
  volume = 48,
  number = 1,
  pages = {1--40}
}

@article{Bender_Augustin_Blettner_2005,
  author = {Ralf Bender and Thomas Augustin and Maria Blettner},
  title = {Generating Survival Times to Simulate {Cox} Proportional Hazards Models},
  year = {2005},
  journal = {Statistics in Medicine},
  volume = {24},
  number = {11},
  DOI = {10.1002/sim.2059},
  pages = {1713--1723}
}

@article{Moest_Hothorn_2015,
  title = {Conditional Transformation Models for Survivor
           Function Estimation},
  author = {Lisa M\"ost and Torsten Hothorn},
  journal = {International Journal of Biostatistics},
  year = {2015},
  volume = 11,
  number = 1,
  pages = {23--50},
  doi = {10.1515/ijb-2014-0006}
}

@article{Moest_Schmid_Faschingbauer_2014,
  author = {Lisa M\"ost and Matthias Schmid and
            Florian Faschingbauer and Torsten Hothorn},
  title = {Predicting Birth Weight with Conditionally Linear
           Transformation Models},
  journal = {Statistical Methods in Medical Research},
  year = {2016},
  volume = 25,
  number = 6,
  pages = {2781--2810},
  DOI = {10.1177/0962280214532745}
}
 
@article{Yang_Prentice_2015,
  author = {Song Yang and Ross L. Prentice},
  title = {Assessing Potentially Time-dependent Treatment Effect From 
           Clinical Trials and Observational Studies for Survival Data, 
           With Applications to the {Women's} {Health} {Initiative} Combined Hormone Therapy Trial},
  year = {2015},
  journal = {Statistics in Medicine},
  volume = {34},
  number = {11},
  DOI = {10.1002/sim.6453},
  pages = {1801--1817}
}

@article{Yu_2004,
  author = {Jun Yu},
  title = {Empirical Characteristic Function Estimation and Its Applications},
  year = {2004},
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  volume = {23},
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@techreport{Hothorn_Moest_Buehlmann_2016,
  author = {Torsten Hothorn and Lisa M\"ost and Peter B\"uhlmann},
  title = {Most Likely Transformations (Extended Version)},
  year = {2017},
  institution = {arXiv 1508.06749},
  url = {http://arxiv.org/abs/1508.06749}
}

@article{Hothorn_Moest_Buehlmann_2017,
  author = {Torsten Hothorn and Lisa M\"ost and Peter B\"uhlmann},
  title = {Most Likely Transformations},
  year = {2018},
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  volume = 45,
  number = 1,
  pages = {110--134}
}
 
@Article{         Parish,
  author        = {W. L. Parish and E. O. Laumann},
  title         = {Chinese Health and Family Life Survey},
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  note          = {Accecibilty verified on May 18, 2009},
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@article{Hothorn_Mueller_Held_2015,
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@book{Therneau_Grambsch_2000,
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    title = {Modern Applied Statistics with S},
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@book{Scheike_Martinussen_2006,
  author = {Thomas H. Scheike and Torben Martinussen},
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    title = {Smoothing Spline ANOVA Models: {R} Package {gss}},
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@article{Yee_2010,
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@book{Fahrmeir_Kneib_Lang_2013,
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@article{Hutmacher_French_2011,
  author = {Matthew M. Hutmacher and Jonathan L. French and Sriram Krishnaswami
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@article{Maruo_Yamaguchi_2017,
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@article{Sauter_Held_2016,
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@article{McLain_Ghosh_2013,
  author = {Alexander C. McLain and Sujit K. Ghosh},
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@Article{tamasi2021tramme,
  author = {B{\'a}lint Tam{\'a}si and Torsten Hothorn},
  title = {{tramME: Mixed-Effects Transformation Models Using Template
          Model Builder}},
  year = {2021},
  journal = {{The R Journal}},
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@Article{Tamasi_Crowther_Puhan_2022,
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            Ewout Steyerberg and Torsten Hothorn},
  title = {Individual Participant Data Meta-analysis with 
           Mixed-effects Transformation Models},
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@Article{pkg:glmmTMB,
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  title = {{glmmTMB Balances Speed and Flexibility Among Packages for
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  year = {2017},
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  number = {2}
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@TechReport{Dandl_2025_nami,
	title={Nonparanormal Adjusted Marginal Inference}, 
	author={Susanne Dandl and Torsten Hothorn},
	year={2025},
	institution={arXiv 2503.01657},
	doi={10.48550/arXiv.2503.01657} 
}

@article{Farouki_2012,
  author = {Rida T. Farouki},
  title = {The {Bernstein} Polynomial Basis: A Centennial Retrospective},
  year = {2012},
  journal = {Computer Aided Geometric Design},
  volume = {29},
  number = {6},
  DOI = {10.1016/j.cagd.2012.03.001},
  pages = {379--419}
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@article{Tsiatis_Davidian_2008,
  author = {Tsiatis, Anastasios A. and Davidian, Marie and Zhang, Min and Lu, Xiaomin},
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           Clinical Trials: A Principled Yet Flexible Approach},
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@TechReport{VanLancker_2024,
    title={Automated, Efficient and Model-free Inference for Randomized Clinical
           Trials via Data-driven Covariate Adjustment}, 
    author={Kelly {Van Lancker} and Iv{\'a}n D{\'i}az and Stijn Vansteelandt},
    year={2024},
    institution = {arXiv 2404.11150},
    doi = {10.48550/arXiv.2404.11150}
}

@article{Zhang_Tsiatis_2008,
  title={Improving Efficiency of Inferences in Randomized Clinical Trials using Auxiliary Covariates},
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@article{Lu_Tsiatis_2008,
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@TechReport{	  hothorn_2024,
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  year		= {2024},
  institution	= {arXiv 2408.17346},
  doi           = {10.48550/arXiv.2408.17346}
}

@TechReport{      senn_2024,
  author        = {Stephen Senn and Franz K\"onig and Martin Posch},
  title         = {Stratification in Randomised Clinical Trials and Analysis of Covariance: Some Simple Theory and Recommendations},
  year          = {2024},
  institution   = {arXiv 2408.06760},
  doi           = {10.48550/arXiv.2408.06760}
}


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@Article{	  dandl_heterogeneous_2024,
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@Article{	  klein_hothorn_barbanti_2020,
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@Article{	  sewak_hothorn_2023,
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@Article{	  van_lancker_covariate_2024,
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@Article{	  partridge_flies_1981,
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@article{barco_virdone_enoxaparin_2023,
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	year = {2023},
	doi = {10.1016/j.thromres.2023.08.009},
	author = {Stefano Barco and Saverio Virdone and Andrea Götschi and Walter Ageno and Juan I. Arcelus and Roland Bingisser and Giuseppe Colucci and Frank Cools and Daniel Duerschmied and Harry Gibbs and Riccardo M. Fumagalli and Bernhard Gerber and Sylvia Haas and Jelle C.L. Himmelreich and Richard Hobbs and Lukas Hobohm and Barry Jacobson and Gloria Kayani and Renato D. Lopes and Peter MacCallum and Evy Micieli and Marc Righini and Helia Robert-Ebadi and Ana Thereza Rocha and Thomas Rosemann and Jitendra Sawhney and Sebastian Schellong and Tim Sebastian and David Spirk and Stefan Stortecky and Alexander G.G. Turpie and Davide Voci and Nils Kucher and Karen Pieper and Ulrike Held and Ajay K. Kakkar}
}

@article{barco_etal_ovid_2022,
	title = {Enoxaparin for Primary Thromboprophylaxis in Symptomatic Outpatients With {COVID-19} ({OVID}): {A} Randomised, Open-Label, Parallel-Group, Multicentre, Phase 3 Trial},
	journal = {The Lancet Haematology},
	volume = {9},
	number = {8},
	pages = {e585-e593},
	year = {2022},
	doi = {10.1016/S2352-3026(22)00175-2},
	author = {Stefano Barco and Davide Voci and Ulrike Held and Tim Sebastian and Roland Bingisser and Giuseppe Colucci and Daniel Duerschmied and André Frenk and Bernhard Gerber and Andrea Götschi and Stavros V Konstantinides and François Mach and Helia Robert-Ebadi and Thomas Rosemann and Noemi R Simon and Hervé Spechbach and David Spirk and Stefan Stortecky and Lukas Vaisnora and Marc Righini and Nils Kucher and Stefano Barco and Davide Voci and Ulrike Held and Tim Sebastian and Roland Bingisser and Giuseppe Colucci and Daniel Duerschmied and André Frenk and Bernhard Gerber and Andrea Götschi and Stavros V Konstantinides and François Mach and Helia Robert-Ebadi and Thomas Rosemann and Noemi R Simon and Hervé Spechbach and David Spirk and Stefan Stortecky and Lukas Vaisnora and Marc Righini and Nils Kucher and Stéphanie {Roth Zetzsche} and Rebecca Spescha and Claudia Leeger and Yulia Butscheid and Eliane Probst and Evy Micieli and Gabor Forgo and Fabian Johner and Alexandru Grigorean and Georgios Vatsakis and Dagmar {Keller Lang} and Silvana {Rampini Speck} and Barbara Hasse and Marco Rueegg and Isabelle Arnold and Christian Nickel and Jeannette Busch and Marc Blondon and Frédéric Glauser and Micol G Cittone and Chiara Kessler and Diona Gjermeni and Christoph B Olivier and Nadine Gauchel and Paul Biever and Lukas Hobohm and Dorothea Becker and Marc Schindewolf and Arnaud Kuenzi and Silvia Ulrich},
}


@article{cools_etal_ethic_2022,
	title = {Thromboprophylactic Low-Molecular-Weight Heparin Versus Standard of Care in Unvaccinated, At-Risk Outpatients With {COVID-19} ({ETHIC}): {A}n Open-Label, Multicentre, Randomised, Controlled, Phase 3b Trial},
	journal = {The Lancet Haematology},
	volume = {9},
	number = {8},
	pages = {e594-e604},
	year = {2022},
	doi = {10.1016/S2352-3026(22)00173-9},
	author = {Frank Cools and Saverio Virdone and Jitendra Sawhney and Renato D Lopes and Barry Jacobson and Juan I Arcelus and F D Richard Hobbs and Harry Gibbs and Jelle C L Himmelreich and Peter MacCallum and Sebastian Schellong and Sylvia Haas and Alexander G G Turpie and Walter Ageno and Ana Thereza Rocha and Gloria Kayani and Karen Pieper and Ajay K Kakkar and Ajit Avhad and Murillo Antunes and Ana {Thereza Rocha} and Jesus {Gonzales Lama} and Atul Abyankar and Adrian Paulo Morales Kormann and Louis {Van Zyl} and Upendra Kaul and Frances Adams and Ivan Aloysius and Matthew Capehorn and Pradeep Kumar and Rajesh Mahajan}
}


@article{Jreskog_polychoric_1994,
	title = {On the Estimation of Polychoric Correlations and their Asymptotic Covariance Matrix},
	volume = {59},
	DOI = {10.1007/bf02296131},
	number = {3},
	journal = {Psychometrika},
	publisher = {Cambridge University Press (CUP)},
	author = {J\"{o}reskog,  Karl G.},
	year = {1994},
	pages = {381-389}
}
