Please use this identifier to cite or link to this item: https://doi.org/10.1198/016214504000000368
Title: Propriety of the posterior distribution and existence of the MLE for regression models with covariates missing at random
Authors: Chen, M.-H.
Ibrahim, J.G.
Shao, Q.-M. 
Keywords: Binary regression model
Covariate distribution
Design matrix
Generalized linear model
Maple
Missing at random
Necessary and sufficient conditions
Noninformative prior
Parametric survival model
Poisson regression model
Issue Date: Jun-2004
Citation: Chen, M.-H., Ibrahim, J.G., Shao, Q.-M. (2004-06). Propriety of the posterior distribution and existence of the MLE for regression models with covariates missing at random. Journal of the American Statistical Association 99 (466) : 421-438. ScholarBank@NUS Repository. https://doi.org/10.1198/016214504000000368
Abstract: Characterizing model identifiability in the presence of missing covariate data is a very important issue in missing data problems. In this article, we characterize the propriety of the posterior distribution of the regression coefficients for some general classes of regression models, including the class of generalized linear models (GLM's) and parametric survival models with right-censored data. Toward this goal, we derive some very general and easy-to-check conditions for the matrix of covariates. We also derive sufficient conditions for the existence of the maximum likelihood estimates and establish novel results for checking propriety of the posterior when the sample size is large. Several theorems are given to establish propriety of the posterior and the existence of the maximum likelihood estimator. The conditions reduce to solving a system of linear equations, which can be carried out using software such as MAPLE, IMSL, or SAS. We assume that the missing covariates are missing at random and assume an improper uniform prior for the regression coefficients. In addition, we establish these results assuming a very general form for the covariate distribution, allowing for both missing categorical and/or continuous covariates. A small dataset is used to illustrate that the posterior can be improper based on complete cases while proper when all of the cases are used in the analysis. Two real datasets are presented to demonstrate verification of posterior propriety for GLM's and parametric survival models, and also to illustrate propriety for large datasets.
Source Title: Journal of the American Statistical Association
URI: http://scholarbank.nus.edu.sg/handle/10635/103983
ISSN: 01621459
DOI: 10.1198/016214504000000368
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