Pseudo-partial likelihood estimators for the Cox regression model 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...

Penalized Estimators in Cox Regression Model

The proportional hazard Cox regression models play a key role in analyzing censored survival data. We use penalized methods in high dimensional scenarios to achieve more efficient models. This article reviews the penalized Cox regression for some frequently used penalty functions. Analysis of medical data namely ”mgus2” confirms the penalized Cox regression performs better than the cox regressi...

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...

A pseudo-Bayesian shrinkage approach to regression with missing covariates.

We consider the linear regression of outcome Y on regressors W and Z with some values of W missing, when our main interest is the effect of Z on Y, controlling for W. Three common approaches to regression with missing covariates are (i) complete-case analysis (CC), which discards the incomplete cases, and (ii) ignorable likelihood methods, which base inference on the likelihood based on the obs...

Semiparametric Maximum Likelihood Estimation in Parametric Regression with Missing Covariates