A common problem in applied regression analysis is that covariate values may be missing for some observations but imputed values may be available. This situation generates a trade-off between bias and precision: the complete cases are often disarmingly few, but replacing the missing observations...

A common problem in applied regression analysis is that covariate values may be missing for some observations but imputed values may be available. This situation generates a trade-off between bias and precision: the complete cases are often disarmingly few, but replacing the missing observations...

A common problem in applied regression analysis is that covariate values may be missing for some observations but imputed values may be available. This situation generates a trade-off between bias and precision: the complete cases are often disarmingly few, but replacing the missing observations...

We address the problem of estimating generalized linear models when some covariate values are missing but imputations are available to fill-in the missing values. This situation generates a bias-precision trade-off in the estimation of the model parameters. Extending the generalized...

In this dissertation, we study three problems related to statisticalinference under order restrictions of the unknown parameters. Thefirst two deal with Cox regression models with missing data, where the baseline cumulative function satisfies a monotone restriction, and the third problem...

In clinical and epidemiological studies, matched case-control designs have been used extensively to investigate the relationships between disease/response and exposure/covariate. Due to the retrospective nature of the study, some covariates may not be observed for all study subjects and missing...