Maximum likelihood estimation in random effects cure rate models with nonignorable missing covariates

Sieve Maximum Likelihood Estimation for Regression Models With Covariates Missing at Random

Missing covariates are common in regression problems. We propose a new semiparametric method based on a fully nonparametric distribution for the missing covariates that are assumed to be missing at random. The method of sieve maximum likelihood estimation is used to obtain the estimators of the regression coefficients. These estimators are shown to be consistent and asymptotically normal with t...

Semiparametric Maximum Likelihood Estimation in Parametric Regression with Missing Covariates

Maximum likelihood analysis of generalized linear models with missing covariates.

Missing data is a common occurrence in most medical research data collection enterprises. There is an extensive literature concerning missing data, much of which has focused on missing outcomes. Covariates in regression models are often missing, particularly if information is being collected from multiple sources. The method of weights is an implementation of the EM algorithm for general maximu...

Likelihood-based Inference with Nonignorable Missing Responses and Covariates in Models for Discrete Longitudinal Data

We propose methods for estimating parameters in two types of models for discrete longitudinal data in the presence of nonignorable missing responses and covariates. We first present the generalized linear model with random effects, also known as the generalized linear mixed model. We specify a missing data mechanism and a missing covariate distribution and incorporate them into the complete dat...

Maximum Likelihood Estimation in Graphical Models with Missing Values