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@Article{HOLLAND1986,
author = {Holland, P. W.},
title = {Statistics and Causal Inference},
journal = {Journal of the American Statistical Association},
volume = {81},
number = {396},
pages = {945-960},
year = {1986},
abstract = {},
location = {},
keywords = {}}
@Article{KACIROTO2014,
author = {Kaciroti, N. A. and Raghunathan, T. E.},
title = {Bayesian sensitivity analysis of incomplete data: Bridging pattern-mixture and selection models.},
journal = {Statistics in Medicine},
volume = {33},
number = {27},
pages = {4841-4857},
year = {2014},
location = {},
keywords = {}}
@phdthesis{JOLANI2012,
author = {Jolani, S.},
title = {Dual Imputation Strategies for Analyzing Incomplete Data},
school = {University of Utrecht},
address = {Utrecht},
pages = {},
year = {2012},
abstract = {},
keywords = {}}
@Article{GALIMARD2016,
author = {Galimard, J. E. and Chevret, S. and Protopopescu, C. and Resche-Rigon, M.},
title = {A multiple imputation approach for {MNAR} mechanisms compatible with {H}eckman's model.},
journal = {Statistics in Medicine},
volume = {35},
number = {17},
pages = {2907-2920},
year = {2016},
location = {},
keywords = {}}
@Article{LITTLE2017,
author = {Little, R. J. A. and Rubin, D. B. and Zangeneh, S. Z.},
title = {Conditions for ignoring the missing-data mechanism in likelihood inferences for parameter subsets},
journal = {Journal of the American Statistical Association},
volume = {112},
number = {517},
pages = {314-320},
year = {2017},
location = {},
keywords = {}}
@Article{LIUBLINSKA2014,
author = {Liublinska, V. and Rubin, D. B.},
title = {Sensitivity analysis for a partially missing binary outcome in a two-arm randomized clinical trial},
journal = {Statistics in Medicine},
volume = {33},
number = {24},
pages = {4170-4185},
year = {2014},
location = {},
keywords = {}}
@Article{PERMUTT2016,
author = {Permutt, T.},
title = {Sensitivity analysis for missing data in regulatory submissions},
journal = {Statistics in Medicine},
volume = {35},
number = {17},
pages = {2876-2879},
year = {2016},
location = {},
keywords = {}}
@Article{SCHOUTEN2017,
author = {Schouten, R. M. and Vink, G.},
title = {Wrapper function `parlMICE`},
journal = {},
volume = {},
number = {},
pages = {\url{https://gerkovink.github.io/parlMICE/Vignette_parlMICE.html}},
year = {2017},
abstract = {},
location = {},
keywords = {}}
@Article{GORDON2014,
author = {Gordon, M.},
title = {Parallel computation of multiple imputation by using `mice` {R} package},
journal = {https://stackoverflow.com/questions/24040280/parallel-computation-of-multiple-imputation-by-using-mice-r-package},
volume = {},
number = {},
pages = {},
year = {2014},
abstract = {},
location = {},
keywords = {}}
@Article{SHORTREED2014,
author = {Shortreed, S. M. and Laber, E. and Scott Stroup, T. and Pineau, J. and Murphy, S. A.},
title = {A multiple imputation strategy for sequential multiple assignment randomized trials},
journal = {Statistics in Medicine},
volume = {33},
number = {24},
pages = {4202-4214},
year = {2014},
location = {},
keywords = {}}
@Article{NAAKTGEBOREN2016,
author = {Naaktgeboren, C. A. and De Groot, J. A. H. and Rutjes, A. W. S. and Bossuyt, P. M. M. and Reitsma, J. B. and Moons, K. G. M.},
title = {Anticipating missing reference standard data when planning diagnostic accuracy studies},
journal = {British Medical Journal},
volume = {352},
pages = {i402},
year = {2016},
location = {},
keywords = {}}
@Article{TODDLITTLE2013,
author = {Little, T. D. and Rhemtulla, M.},
title = {Planned missing data designs for developmental researchers},
journal = {Child Development Perspectives},
volume = {7},
number = {4},
pages = {199-204},
year = {2013},
location = {},
keywords = {}}
@Article{RHEMTULLA2016,
author = {Rhemtulla, M. and Hancock, G. R.},
title = {Planned missing data designs in educational psychology research},
journal = {Educational Psychologist},
volume = {51},
number = {3-4},
pages = {305-316},
year = {2016},
location = {},
keywords = {}}
@Incollection{KENWARD2015,
author = {Kenward, M. G. and Molenberghs, G.},
title = {A Perspective and Historical Overview on Selection, Pattern-Mixture
and Shared Parameter Models},
booktitle = {Handbook of Missing Data Methodology},
editor = {Molenberghs, G. and Fitzmaurice, G. M. and Kenward, M. G. and Tsiatis, A. A. and Verbeke, G.},
publisher = {Chapman \& Hall/CRC Press},
address = {Boca Raton, FL},
pages = {53-89},
year = {2015},
abstract = {},
keywords = {}}
@Incollection{GOLDSTEIN2015,
author = {Goldstein, H. and Carpenter, J. R.},
title = {Multilevel multiple imputation},
booktitle = {Handbook of Missing Data Methodology},
editor = {Molenberghs, G. and Fitzmaurice, G. M. and Kenward, M. G. and Tsiatis, A. A. and Verbeke, G.},
publisher = {Chapman \& Hall/CRC Press},
address = {Boca Raton, FL},
pages = {295-316},
year = {2015},
abstract = {},
keywords = {}}
@Article{YU2017,
author = {Yu, M. and Reiter, J. P. and Zhu, L. and Liu, B. and Cronin, K. A. and Feuer, E. J.},
title = {Protecting Confidentiality in Cancer Registry Data With Geographic Identifiers},
journal = {American Journal of Epidemiology},
volume = {186},
number = {1},
pages = {83-91},
year = {2017},
location = {},
keywords = {}}
@Article{DRECHSLER2010,
author = {Drechsler, J. and Reiter, J. P.},
title = {Sampling with synthesis: A new approach for releasing public use census microdata},
journal = {Journal of the American Statistical Association},
volume = {105},
number = {492},
pages = {1347-1357},
year = {2010},
location = {},
keywords = {}}
@Article{REITER2014,
author = {Reiter, J. P. and Wang, Q. and Zhang, B.},
title = {Bayesian estimation of disclosure risks for multiply imputed, synthetic data},
journal = {Journal of Privacy and Confidentiality},
volume = {6},
number = {1},
pages = {2},
year = {2014},
location = {},
keywords = {}}
@Article{LOONG2017,
author = {Loong, B. and Rubin, D. B.},
title = {Multiply-Imputed Synthetic Data: Advice to the Imputer},
journal = {Journal of Official Statistics},
volume = {33},
number = {4},
pages = {1005-1019},
year = {2017},
location = {},
keywords = {}}
@Article{SCHONBECK2013,
author = {Sch\"onbeck, Y. and Talma, H. and Van Dommelen, P. and Bakker, B. and Buitendijk, S. E. and HiraSing, R. A. and Van Buuren, S.},
title = {The world’s tallest nation has stopped growing taller: The height of {D}utch children from 1955 to 2009},
journal = {Pediatric Research},
volume = {73},
number = {3},
pages = {371-377},
year = {2013},
location = {},
keywords = {}}
@Manual{MITML,
title = {`mitml`: Tools for Multiple Imputation in Multilevel Modeling},
author = {Grund, S. and Robitzsch, A. and L\"{u}dtke, O.},
year = {2018},
note = {R package version 0.3-5.7},
}
@Article{GRUND2018B,
author = {Grund, S. and L\"udtke, O. and Robitzsch, A.},
title = {Multiple Imputation of Missing Data at Level 2: A Comparison of Fully Conditional and Joint Modeling in Multilevel Designs},
journal = {Journal of Educational and Behavioral Statistics},
volume = {doi.org/10.3102/1076998617738087},
pages = {},
year = {2018},
location = {},
keywords = {}}
@Article{ZHANG2017,
author = {Zhang, Q. and Wang, L.},
title = {Moderation analysis with missing data in the predictors.},
journal = {Psychological Methods},
volume = {22},
number = {4},
pages = {649-666},
year = {2017},
location = {},
keywords = {}}
@Article{RABE2002,
author = {Rabe-Hesketh, S. and Skrondal, A. and Pickles, A.},
title = {Reliable estimation of generalized linear mixed models using adaptive quadrature},
journal = {The Stata Journal},
volume = {2},
number = {1},
pages = {1-21},
year = {2002},
location = {},
keywords = {}}
@Article{TALJAARD2008,
author = {Taljaard, M. and Donner, A. and Klar, N.},
title = {Imputation strategies for missing continuous outcomes in cluster randomized trials},
journal = {Biometrical Journal},
volume = {50},
number = {3},
pages = {329-345},
year = {2008},
location = {},
keywords = {}}
@Book{GOLDSTEIN2011B,
author = {Goldstein, H.},
title = {Multilevel Statistical Models},
edition = {4th},
pages = {},
editor = {},
publisher = {John Wiley \& Sons},
address = {Chichester, UK},
year = {2011},
keywords = {}}
@Article{HILL1998,
author = {Hill, P. W. and Goldstein, H.},
title = {Multilevel modeling of educational data with cross-classification and missing identification for units},
journal = {Journal of Educational and Behavioral Statistics},
volume = {23},
number = {2},
pages = {117-128},
year = {1998},
location = {},
keywords = {}}
@Article{LONG2015,
author = {Long, Q. and Johnson, B. A.},
title = {Variable selection in the presence of missing data: Resampling and imputation},
journal = {Biostatistics},
volume = {16},
number = {3},
pages = {596-610},
year = {2015},
location = {},
keywords = {}}
@Article{LIU2016,
author = {Liu, Y. and Wang, Y. and Feng, Y. and Wall, M. M.},
title = {Variable selection and prediction with incomplete high-dimensional data},
journal = {The Annals of Applied Statistics},
volume = {10},
number = {1},
pages = {418},
year = {2016},
location = {},
keywords = {}}
@Article{MARINO2017,
author = {Marino, M. and Buxton, O. M. and Li, Y.},
title = {Covariate selection for multilevel models with missing data},
journal = {Stat},
volume = {6},
number = {1},
pages = {31-46},
year = {2017},
location = {},
keywords = {}}
@Article{ZHAO2017,
author = {Zhao, Y. and Long, Q.},
title = {Variable selection in the presence of missing data: Imputation-based methods},
journal = {Wiley Interdisciplinary Reviews: Computational Statistics},
volume = {9},
number = {5},
pages = {},
year = {2017},
location = {},
keywords = {}}
@Article{TIBSHIRANI1996,
author = {Tibshirani, R. J.},
title = {Regression shrinkage and selection via the lasso},
journal = {Journal of the Royal Statistical Society. Series B (Methodological)},
volume = {},
number = {},
pages = {267-288},
year = {1996},
location = {},
keywords = {}}
@Article{MUSORO2014,
author = {Musoro, J. Z. and Zwinderman, A. H. and Puhan, M. A. and Ter Riet, G. and Geskus, R. B.},
title = {Validation of prediction models based on lasso regression with multiply imputed data},
journal = {BMC Medical Research Methodology},
volume = {14},
number = {1},
pages = {116},
year = {2014},
location = {},
keywords = {}}
@Article{CHEN2013,
author = {Chen, Q. and Wang, S.},
title = {Variable selection for multiply-imputed data with application to dioxin exposure study},
journal = {Statistics in Medicine},
volume = {32},
number = {21},
pages = {3646-3659},
year = {2013},
location = {},
keywords = {}}
@Article{BONDARENKO2016,
author = {Bondarenko, I. and Raghunathan, T. E.},
title = {Graphical and numerical diagnostic tools to assess suitability of multiple imputations and imputation models},
journal = {Statistics in medicine},
volume = {35},
number = {17},
pages = {3007-3020},
year = {2016},
location = {},
keywords = {}}
@inbook{TEMPL2011B,
author = {Templ, M. and Hron, K. and Filzmoser, P.},
publisher = {Wiley-Blackwell},
title = {robCompositions: An R‐package for Robust Statistical Analysis of Compositional Data},
booktitle = {Compositional Data Analysis},
chapter = {25},
pages = {341-355},
year = {2011},
}
@Article{EEKHOUT2018,
author = {Eekhout, I. and De Vet, H. C. W. and De Boer, M. R. and Twisk, J. W. R. and Heymans, M. W.},
title = {Passive imputation and parcel summaries are both valid to handle missing items in studies with many multi-item scales},
journal = {Statistical Methods in Medical Research},
volume = {27},
number = {4},
pages = {1128-1140},
year = {2018},
location = {},
keywords = {}}
@Article{PLUMPTON2016,
author = {Plumpton, C. O. and Morris, T. P. and Hughes, D. A. and White, I. R.},
title = {Multiple imputation of multiple multi-item scales when a full imputation model is infeasible},
journal = {BMC Research Notes},
volume = {9},
number = {1},
pages = {45},
year = {2016},
location = {},
keywords = {}}
@Article{SOVILJ2016,
author = {Sovilj, D. and Eirola, E. and Miche, Y. and Bj\"ork, K-M. and Nian, R. and Akusok, A. and Lendasse, A.},
title = {Extreme learning machine for missing data using multiple imputations},
journal = {Neurocomputing},
volume = {174},
number = {},
pages = {220-231},
year = {2016},
location = {},
keywords = {}}
@Article{TUTZ2015,
author = {Tutz, G. and Ramzan, S.},
title = {Improved methods for the imputation of missing data by nearest neighbor methods},
journal = {Computational Statistics \& Data Analysis},
volume = {90},
number = {},
pages = {84-99},
year = {2015},
location = {},
keywords = {}}
@phdthesis{VINK2015B,
author = {Vink, G.},
title = {Restrictive Imputation of Incomplete Survey Data},
school = {Utrecht University},
address = {},
pages = {},
year = {2015},
keywords = {}}
@Article{VINK2013,
author = {Vink, G. and Van Buuren, S.},
title = {Multiple Imputation of Squared Terms},
journal = {Sociological Methods \& Research},
volume = {42},
number = {4},
pages = {598-607},
year = {2013},
location = {},
keywords = {}}
@Book{BELLE2002,
author = {Van Belle, G.},
title = {Statistical Rules of Thumb},
volume = {},
pages = {},
editor = {},
publisher = {John Wiley \& Sons},
address = {New York},
year = {2002},
keywords = {}}
@Article{AUDIGIER2016,
author = {Audigier, V. and Husson, F. and Josse, J.},
title = {Multiple imputation for continuous variables using a {B}ayesian principal component analysis},
journal = {Journal of Statistical Computation and Simulation},
volume = {86},
number = {11},
pages = {2140-2156},
year = {2016},
location = {},
keywords = {}}
@Article{LI2014,
author = {Li, F. and Baccini, M. and Mealli, F. and Zell, E. R. and Frangakis, C. E. and Rubin, D. B.},
title = {Multiple imputation by ordered monotone blocks with application to the anthrax vaccine research program},
journal = {Journal of Computational and Graphical Statistics},
volume = {23},
number = {3},
pages = {877-892},
year = {2014},
location = {},
keywords = {}}
@phdthesis{ZHU2016,
author = {Zhu, J.},
title = {Assessment and Improvement of a Sequential Regression Multivariate Imputation Algorithm},
school = {University of Michigan},
address = {},
pages = {},
year = {2016},
keywords = {}}
@Article{LEE2018,
author = {Lee, M. and Rahbar, M. H. and Brown, M. and Gensler, L. and Weisman, M. and Diekman, L. and Reveille, J. D.},
title = {A multiple imputation method based on weighted quantile regression models for longitudinal censored biomarker data with missing values at early visits},
journal = {BMC Medical Research Methodology},
volume = {18},
number = {1},
pages = {8},
year = {2018},
location = {},
keywords = {}}
@Article{NGUYEN2017,
author = {Nguyen, C. D. and Carlin, J. B. and Lee, K. J.},
title = {Model checking in multiple imputation: an overview and case study},
journal = {Emerging Themes in Epidemiology},
volume = {14},
number = {1},
pages = {8},
year = {2017},
location = {},
keywords = {}}
@Article{KROPKO2014,
author = {Kropko, J. and Goodrich, B. and Gelman, A. and Hill, J.},
title = {Multiple imputation for continuous and categorical data: Comparing joint multivariate normal and conditional approaches},
journal = {Political Analysis},
volume = {22},
number = {4},
pages = {497-519},
year = {2014},
location = {},
keywords = {}}
@Article{SEAMAN2016,
author = {Seaman, S. R. and Hughes, R. A.},
title = {Relative efficiency of joint-model and full-conditional-specification multiple imputation when conditional models are compatible: The general location model},
journal = {Statistical Methods in Medical Research},
volume = {doi.org/10.1177/0962280216665872},
number = {},
pages = {},
year = {2018},
location = {},
keywords = {}}
@Article{LEE2012,
author = {Lee, K. J. and Galati, J. C. and Simpson, J. A. and Carlin, J. B.},
title = {Comparison of methods for imputing ordinal data using multivariate normal imputation: a case study of non‐linear effects in a large cohort study},
journal = {Statistics in Medicine},
volume = {31},
number = {30},
pages = {4164-4174},
year = {2012},
location = {},
keywords = {}}
@Article{ZHU2015,
author = {Zhu, J. and Raghunathan, T. E.},
title = {Convergence properties of a sequential regression multiple imputation algorithm},
journal = {Journal of the American Statistical Association},
volume = {110},
number = {511},
pages = {1112-1124},
year = {2015},
location = {},
keywords = {}}
@Article{LIU2013,
author = {Liu, J. and Gelman, A. and Hill, J. and Su, Y. S. and Kropko, J.},
title = {On the stationary distribution of iterative imputations},
journal = {Biometrika},
volume = {101},
number = {1},
pages = {155-173},
year = {2013},
abstract = {},
location = {},
keywords = {}}
@Article{HUGHES2014,
author = {Hughes, R. A. and White, I. R. and Seaman, S. R. and Carpenter, J. R. and Tilling, K. and Sterne, J. A. C.},
title = {Joint modelling rationale for chained equations},
journal = {BMC Medical Research Methodology},
volume = {14},
number = {1},
pages = {28},
year = {2014},
location = {},
keywords = {}}
@Article{LI2012,
author = {Li, F. and Yu, Y. and Rubin, D. B.},
title = {Imputing Missing Data by Fully Conditional Models: Some Cautionary Examples and Guideline},
journal = {Duke University Department of Statistical Science Discussion Paper},
volume = {11-24},
number = {},
pages = {},
year = {2012},
location = {},
keywords = {}}
@Article{YAO2014,
author = {Yao, Y. and Chen, S-C. and Wang, S-H.},
title = {On compatibility of discrete full conditional distributions: A graphical representation approach},
journal = {Journal of Multivariate Analysis},
volume = {124},
number = {},
pages = {1-9},
year = {2014},
location = {},
keywords = {}}
@Article{KUO2017,
author = {Kuo, K-L. and Song, C-C. and Jiang, T. J.},
title = {Exactly and almost compatible joint distributions for high-dimensional discrete conditional distributions},
journal = {Journal of Multivariate Analysis},
volume = {157},
number = {},
pages = {115-123},
year = {2017},
location = {},
keywords = {}}
@Article{ERLER2016,
author = {Erler, N. S. and Rizopoulos, D. and Van Rosmalen, J. and Jaddoe, V. W. V. and Franco, O. H. and Lesaffre, E. M.},
title = {Dealing with missing covariates in epidemiologic studies: A comparison between multiple imputation and a full {B}ayesian approach.},
journal = {Statistics in Medicine},
volume = {35},
number = {17},
pages = {2955-2974},
year = {2016},
location = {},
keywords = {}}
@Article{ERLER2018,
author = {Erler, N. S. and Rizopoulos, D. and Jaddoe, V. W. V. and Franco, O. H. and Lesaffre, E. M.},
title = {Bayesian imputation of time-varying covariates in linear mixed models},
journal = {Statistical Methods in Medical Research},
volume = {to appear},
number = {},
pages = {},
year = {2018},
location = {},
keywords = {}}
@Article{JACKSON2014,
author = {Jackson, D. and White, I. R. and Seaman, S. R. and Evans, H. and Baisley, K. and Carpenter, J. R.},
title = {Relaxing the independent censoring assumption in the Cox proportional hazards model using multiple imputation},
journal = {Statistics in Medicine},
volume = {33},
number = {27},
pages = {4681-4694},
year = {2014},
location = {},
keywords = {}}
@Article{HSU2015,
author = {Hsu, C. H. and Taylor, J. M. G. and Hu, C.},
title = {Analysis of accelerated failure time data with dependent censoring using auxiliary variables via nonparametric multiple imputation},
journal = {Statistics in Medicine},
volume = {34},
number = {19},
pages = {2768-2780},
year = {2015},
location = {},
keywords = {}}
@Article{DELORD2016,
author = {Delord, M. and G\'enin, E.},
title = {Multiple imputation for competing risks regression with interval-censored data},
journal = {Journal of Statistical Computation and Simulation},
volume = {86},
number = {11},
pages = {2217-2228},
year = {2016},
location = {},
keywords = {}}
@Article{LIAW2002,
title = {Classification and Regression by random{F}orest},
author = {Liaw, A. and Wiener, M.},
journal = {R News},
year = {2002},
volume = {2},
number = {3},
pages = {18-22},
url = {http://CRAN.R-project.org/doc/Rnews/},
}
@Manual{THERNEAU2017,
title = {`rpart`: Recursive Partitioning and Regression Trees},
author = {Therneau, T. M. and Atkinson, B. and Ripley, B. D.},
year = {2017},
note = {R package version 4.1-11},
url = {https://CRAN.R-project.org/package=rpart},
}
@Article{KLEINKE2013,
author = {Kleinke, K. and Reinecke, J.},
title = {Multiple imputation of incomplete zero-inflated count data},
journal = {Statistica Neerlandica},
volume = {67},
number = {3},
pages = {311-336},
year = {2013},
location = {},
keywords = {}}
@Article{SHAH2014,
author = {Shah, A. D. and Bartlett, J. W. and Carpenter, J. R. and Nicholas, O. and Hemingway, H.},
title = {Comparison of random forest and parametric imputation models for imputing missing data using {MICE}: A {CALIBER} study},
journal = {American Journal of Epidemiology},
volume = {179},
number = {6},
pages = {764-774},
year = {2014},
location = {},
keywords = {}}
@Article{AUDIGIER2017,
author = {Audigier, V. and Husson, F. and Josse, J.},
title = {{MIMCA}: Multiple imputation for categorical variables with multiple correspondence analysis},
journal = {Statistics and Computing},
volume = {27},
number = {2},
pages = {501-518},
year = {2017},
location = {},
keywords = {}}
@Article{HAPFELMEIER2012,
author = {Hapfelmeier, A. and Hothorn, T. and Ulm, K.},
title = {Recursive partitioning on incomplete data using surrogate decisions and multiple imputation},
journal = {Computational Statistics \& Data Analysis},
volume = {56},
number = {6},
pages = {1552-1565},
year = {2012},
location = {},
keywords = {}}
@Article{VALDIVIEZO2015,
author = {Valdiviezo, H. C. and Van Aelst, S.},
title = {Tree-based prediction on incomplete data using imputation or surrogate decisions},
journal = {Information Sciences},
volume = {311},
number = {},
pages = {163-181},
year = {2015},
location = {},
keywords = {}}
@Article{WALJEE2013,
author = {Waljee, A. K. and Mukherjee, A. and Singal, A. G. and Zhang, Y. and Warren, J. and Balis, U. and Marrero, J. and Zhu, J. and Higgins, P. D. R.},
title = {Comparison of imputation methods for missing laboratory data in medicine},
journal = {BMJ open},
volume = {3},
number = {8},
pages = {e002847},
year = {2013},
abstract = {},
location = {},
keywords = {}}
@Article{STEKHOVEN2011,
author = {Stekhoven, D. J. and B\"uhlmann, P.},
title = {`missForest`: non-parametric missing value imputation for mixed-type data},
journal = {Bioinformatics},
volume = {28},
number = {1},
pages = {112-118},
year = {2011},
abstract = {Abstract Motivation: Modern data acquisition based on high-throughput technology is often facing the problem of missing data. Algorithms commonly used in the analysis of such large-scale data often depend on a complete set. Missing value imputation offers a solution to this problem. However, the majority of available imputation methods are restricted to one type of variable only: continuous or categorical. For mixed-type data, the different types are usually handled separately. Therefore, these methods ignore possible relations between variable},
location = {},
keywords = {}}
@Article{VANDERPALM2016A,
author = {Van der Palm, D. W. and Van der Ark, L. A. and Vermunt, J. K.},
title = {A comparison of incomplete-data methods for categorical data.},
journal = {Statistical Methods in Medical Research},
volume = {25},
number = {2},
pages = {754-774},
year = {2016},
location = {},
keywords = {}}
@Article{ZHOU2017,
author = {Zhou, M. and He, Y. and Yu, M. and Hsu, C. H.},
title = {A nonparametric multiple imputation approach for missing categorical data},
journal = {BMC Medical Research Methodology},
volume = {17},
number = {1},
pages = {87},
year = {2017},
location = {},
keywords = {}}
@Article{WU2015,
author = {Wu, W. and Jia, F. and Enders, C. K.},
title = {A comparison of imputation strategies for ordinal missing data on {Likert} scale variables},
journal = {Multivariate Behavioral Research},
volume = {50},
number = {5},
pages = {484-503},
year = {2015},
location = {},
keywords = {}}
@Article{DOOVE2014,
author = {Doove, L. L. and Van Buuren, S. and Dusseldorp, E.},
title = {Recursive Partitioning for Missing Data Imputation in the Presence of Interaction Effects},
journal = {Computational Statistics \& Data Analysis},
volume = {72},
number = {},
pages = {92-104},
year = {2014},
abstract = {},
location = {},
keywords = {}}
@Article{HARDT2013,
author = {Hardt, J. and Herke, M. and Brian, T. and Laubach, W.},
title = {Multiple imputation of missing data: A simulation study on a binary response},
journal = {Open Journal of Statistics},
volume = {3},
number = {5},
pages = {},
year = {2013},
location = {},
keywords = {}}
@Article{AKANDE2017,
author = {Akande, O. and Li, F. and Reiter, J. P.},
title = {An empirical comparison of multiple imputation methods for categorical data},
journal = {The American Statistician},
volume = {71},
number = {2},
pages = {162-170},
year = {2017},
location = {},
keywords = {}}
@Book{GAFFERT2016,
author = {Gaffert, P. and Koller-Meinfelder, F. and Bosch, V.},
title = {Towards an mi-proper predictive mean matching},
volume = {Working Paper},
pages = {},
editor = {},
publisher = {University of Bamberg},
address = {Bamberg, Germany},
year = {2016},
keywords = {}}
@Article{KLEINKE2017,
author = {Kleinke, K.},
title = {Multiple Imputation Under Violated Distributional Assumptions: A Systematic Evaluation of the Assumed Robustness of Predictive Mean Matching},
journal = {Journal of Educational and Behavioral Statistics},
volume = {42},
number = {4},
pages = {371-404},
year = {2017},
location = {},
keywords = {}}
@Article{MORRIS2014,
author = {Morris, T. P. and White, I. R. and Royston, P.},
title = {Tuning multiple imputation by predictive mean matching and local residual draws.},
journal = {BMC Medical Research Methodology},
volume = {14},
number = {},
pages = {75},
year = {2014},
location = {},
keywords = {}}
@Article{RIGBY2005,
title = {Generalized additive models for location, scale and shape,(with discussion)},
journal = {Applied Statistics},
volume = {54},
part = {3},
pages = {507-554},
year = {2005},
author = {Rigby, R. A. and Stasinopoulos, D. M.},
}
@Book{STASINOPOULOS2017,
author = {Stasinopoulos, D. M. and Rigby, R. A. and Heller, G. Z. and Voudouris, V. and De Bastiani, F.},
title = {Flexible Regression and Smoothing},
volume = {},
pages = {},
editor = {},
publisher = {CRC Press},
address = {Boca Raton, FL},
year = {2017},
abstract = {This book is about learning from data using the Generalized Additive Models for Location, Scale and Shape (GAMLSS). GAMLSS extends the Generalized Linear Models (GLMs) and Generalized Additive Models (GAMs) to accommodate large complex datasets, which are increasingly prevalent. GAMLSS allows any parametric distribution for the response variable and modelling all the parameters (location, scale and shape) of the distribution as linear or smooth functions of explanatory variables. This book provides a broad overview of GAMLSS methodology and how it is implemented in R. It includes a comprehensive collection of real data examples, integrated code, and figures to illustrate the methods, and is supplemented by a website with code, data and additional materials.},
keywords = {}}
@Manual{SALFRAN2017,
title = {ImputeRobust: Robust Multiple Imputation with Generalized Additive Models for
Location Scale and Shape},
author = {Salfran, D. and Spiess, M.},
year = {2017},
note = {R package version 1.2},
url = {https://CRAN.R-project.org/package=ImputeRobust},
}
@phdthesis{DEJONG2012,
author = {De Jong, R.},
title = {Robust Multiple Imputation},
school = {University of Hamburg},
address = {Hamburg, Germany},
pages = {},
year = {2012},
abstract = {},
keywords = {}}
@Article{DEJONG2016,
author = {De Jong, R. and Van Buuren, S. and Spiess, M.},
title = {Multiple imputation of predictor variables using generalized additive models},
journal = {Communications in Statistics - Simulation and Computation},
volume = {45},
number = {3},
pages = {968-985},
year = {2016},
abstract = {},
location = {},
keywords = {}}
@Article{VONHIPPEL2013,
author = {Von Hippel, P. T.},
title = {Should a Normal Imputation Model Be Modified to Impute Skewed Variables?},
journal = {Sociological Methods \& Research},
volume = {42},
number = {1},
pages = {105-138},
year = {2013},
abstract = {},
location = {},
keywords = {}}
@Article{SCHOUTEN2018,
author = {Schouten, R. M. and Lugtig, P. L. and Vink, G.},
title = {Generating missing values for simulation purposes: A multivariate amputation procedure},
journal = {},
volume = {Working paper, University of Utrecht},
number = {},
pages = {},
year = {2018},
abstract = {},
location = {},
keywords = {}}
@Article{VONHIPPEL2007,
author = {Von Hippel, P. T.},
title = {Regression with missing $Y$'s: An improved strategy for analyzing multiply imputed data},
journal = {Sociological Methodology},
volume = {37},
number = {1},
pages = {83-117},
year = {2007},