Monte Carlo EM for missing covariates in parametric regression models

Biometrics
J G IbrahimS R Lipsitz

Abstract

We propose a method for estimating parameters for general parametric regression models with an arbitrary number of missing covariates. We allow any pattern of missing data and assume that the missing data mechanism is ignorable throughout. When the missing covariates are categorical, a useful technique for obtaining parameter estimates is the EM algorithm by the method of weights proposed in Ibrahim (1990, Journal of the American Statistical Association 85, 765-769). We extend this method to continuous or mixed categorical and continuous covariates, and for arbitrary parametric regression models, by adapting a Monte Carlo version of the EM algorithm as discussed by Wei and Tanner (1990, Journal of the American Statistical Association 85, 699-704). In addition, we discuss the Gibbs sampler for sampling from the conditional distribution of the missing covariates given the observed data and show that the appropriate complete conditionals are log-concave. The log-concavity property of the conditional distributions will facilitate a straightforward implementation of the Gibbs sampler via the adaptive rejection algorithm of Gilks and Wild (1992, Applied Statistics 41, 337-348). We assume the model for the response given the covariate...Continue Reading

References

Jan 1, 1996·Lifetime Data Analysis·S R Lipsitz, J G Ibrahim

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Citations

Sep 5, 2002·Statistics in Medicine·Michael ParzenSteven Lipshultz
Aug 21, 2009·Journal of the American Statistical Association·Joseph G IbrahimNiansheng Tang
May 1, 2009·Test·Joseph G Ibrahim, Geert Molenberghs
Dec 18, 2009·Computational Statistics & Data Analysis·Hua LiangGuohua Zou
Mar 27, 2009·Journal of Animal Breeding and Genetics = Zeitschrift Für Tierzüchtung Und Züchtungsbiologie·G Yagüe-UtrillaC Moreno
Mar 29, 2008·Biometrics·Gerda Claeskens, Fabrizio Consentino
Feb 7, 2009·Biometrical Journal. Biometrische Zeitschrift·Yang ZhaoDonald L McLeish
Feb 27, 2013·Statistics in Medicine·N SabbeJ-P Ottoy
Jul 29, 2010·Biometrics·Joseph G IbrahimRuixin Guo
Dec 14, 2011·Biometrics·Nanhua Zhang, Roderick J Little
Jul 21, 2010·Statistics in Medicine·Kumar B Rajan, Sue E Leurgans
Feb 13, 2009·Biometrics·Xiaoyan ShiJoseph G Ibrahim
May 13, 2009·Biometrics·Joseph Sexton, Petter Laake
Dec 29, 2009·Scandinavian Journal of Statistics, Theory and Applications·Hongtu ZhuXiaoyan Shi
Jan 27, 2009·Journal of Multivariate Analysis·Qingxia ChenPralay Senchaudhuri
May 5, 2009·Journal of Multivariate Analysis·Hua Liang
May 15, 2016·Biostatistics·Jonathan W Bartlett, Jeremy M G Taylor
Sep 2, 2016·Statistics in Medicine·Rajibul Mian, Sudhir Paul
Jan 1, 2015·Statistical Methods in Medical Research·Wei LiuZhiwei Zhang
Feb 15, 2014·Statistical Methods in Medical Research·Jonathan W BartlettUNKNOWN Alzheimer's Disease Neuroimaging Initiative*
Dec 8, 2016·IEEE Journal of Biomedical and Health Informatics·Guanjin WangKup-Sze Choi
May 12, 2018·Biometrical Journal. Biometrische Zeitschrift·Habtamu K BenechaKalyan Das
Mar 14, 2002·Biometrics·Amy H HerringStuart R Lipsitz
Dec 10, 2013·Statistical Methods in Medical Research·Zhiwei ZhangJun Zhang
Dec 21, 2017·JMIR MHealth and UHealth·Rebecca RobbinsDustin T Duncan
Nov 1, 2018·The International Journal of Biostatistics·Paul W Bernhardt
Mar 14, 2019·Health Information Science and Systems·Sayan Das, Jaya Sil
May 27, 2016·The International Journal of Biostatistics·Michael D Regier, Erica E M Moodie
May 29, 1999·Statistical Methods in Medical Research·N J Horton, N M Laird
Mar 20, 2021·Multivariate Behavioral Research·Sarah Humberg, Simon Grund

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