Statistics and Its Interface

Editorial

Estimation in longitudinal studies with nonignorable dropout

A note on the relationships between multiple imputation, maximum likelihood and fully Bayesian methods for missing responses in linear regression models

A unified theory on empirical likelihood methods for missing data

Parametric fractional imputation for mixed models with nonignorable missing data

Likelihood estimate of treatment effects under selection bias

Estimation and imputation in linear regression with missing values in both response and covariate

Imputation methods for quantile estimation under missing at random

Direction estimation in the single-index model with missing values

Statistics can lie but can also correct for lies: Reducing response bias in NLAAS via Bayesian imputation

A semi-parametric approach for imputing mixed data

Supplement Material for “Statistics can lie but can also correct for lies...”, Liu et al., pp. 387-398.