Maximum likelihood inference for the Cox regression model with applications to missing covariates

Posterior Propriety and Computation for the Cox Regression Model with Applications to Missing Covariates

In this paper, we carry out an in-depth theoretical investigation for Bayesian inference for the Cox regression model (Cox, 1972, 1975). Specifically, we establish establish necessary and sufficient conditions for posterior propriety of the regression coefficients, β, in Cox’s partial likelihood, which can be obtained as the limiting marginal posterior distribution of β through the specificatio...

Semiparametric Maximum Likelihood Estimation in Parametric Regression with Missing Covariates

Cox Regression for Current Status Data with Missing Covariates

Statistical inference based on the right-censored data for proportional hazard (PH) model with missing covariates has received considerable attention, but interval-censored or current status data with missing covariates are not yet investigated. Our study is partly motivated by analysis of fracture data from a cross-sectional study, where the ocurrence time of fracture was interval-censored and...

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...

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...