Multiple imputation is a straightforward method for handling missing data in a principled
fashion. This paper presents an overview of multiple imputation, including important …

The literature of regression analysis with missing values of the independent variables is
reviewed. Six classes of procedures are distinguished: complete case analysis, available …

Motivation: Modern data acquisition based on high-throughput technology is often facing the
problem of missing data. Algorithms commonly used in the analysis of such large-scale data …

The most user-friendly and authoritative resource on missing data has been completely
revised to make room for the latest developments that make handling missing data more …

An up-to-date, comprehensive treatment of a classic text on missing data in statistics The
topic of missing data has gained considerable attention in recent decades. This new edition …

Hidden Markov models—most often abbreviated to the acronym “HMMs”—are one of the
most successful statistical modelling ideas that have came up in the last forty years: the use …

This article describes and evaluates a procedure for imputing missing values for a relatively
complex data structure when the data are missing at random. The imputations are obtained …

Subjects often drop out of longitudinal studies prematurely, yielding unbalanced data with
unequal numbers of measures for each subject. Modern software programs for handling …

When observations in social research are selected so that they are not independent of the
outcome variables in a study, sample selection leads to biased inferences about social …

With contributions from some of the most prominent researchers in the field, this carefully
edited collection provides a clear, comprehensive, and unified overview of recent …