Modern epidemiology often requires testing of the effect of a covariate on multiple end points
from the same study. However, popular state of the art methods for multiple testing require …

As the" father" of multiple imputation (MI), it gives me great pleasure to be able to comment
on this collection of contributions on MI. The nice review by Paul Zhang serves as an …

We establish computationally flexible methods and algorithms for the analysis of multivariate
skew normal models when missing values occur in the data. To facilitate the computation …

Researchers are often faced with the challenge of develo** statistical models with
incomplete data. Exacerbating this situation is the possibility that either the researcher's …

When dealing with incomplete data in statistical learning, or incomplete observations in
probabilistic inference, one needs to distinguish the fact that a certain event is observed from …

There are many methods to enhance survey participation. The most popular methods are
incentives, prenotification, assurances of anonymity, personalization, stating a deadline …

Empirical data analyses often require complete data sets. In the case of incompletely
observed data sets therefore methods are attractive that generate plausible values …

Many types of data are often incompletely observed. How incompletely is typically randomly
determined. Heitjan and Rubin (Annals of Statistics, 1991) proposed a condition,“coarsened …

When are inferences (whether Direct-Likelihood, Bayesian, or Frequentist) obtained from
partial data valid? This article answers this question by offering a new asymptotic theory …

We consider models for the observed realization from a missing-data problem in which the
complete data comprise a random vector and in which arbitrary patterns of observed and …