Missing covariates in logistic regression, estimation and distribution selection

Logistic regression with outcome and covariates missing separately or simultaneously

Estimation methods are proposed for fitting logistic regression in which outcome and covariate variables are missing separately or simultaneously. One of the two proposed estimators is an extension of the validation likelihood estimator of BreslowandCain (1988). The other is a joint conditional likelihood estimator that uses both validation and nonvalidation data. Large sample properties of the...

Inference using conditional logistic regression with missing covariates.

When there are many nuisance parameters in a logistic regression model, a popular method for eliminating these nuisance parameters is conditional logistic regression. Unfortunately, another common problem in a logistic regression analysis is missing covariate data. With many nuisance parameters to eliminate and missing covariates, many investigators exclude any subject with missing covariates a...

Estimation in zero-inflated binomial regression with missing covariates

Semiparametric Maximum Likelihood Estimation in Parametric Regression with Missing Covariates

Model selection for zero-inflated regression with missing covariates

Count data are widely existed in the fields of medical trials, public health, surveys and environmental studies. In analyzing count data, it is important to find outwhether the zeroinflation exists or not and how to select the most suitable model. However, the classic AIC criterion formodel selection is invalid when the observations aremissing. In this paper, we develop a new model selection cr...