This article reviews recent advances in missing data research using graphical models to
represent multivariate dependencies. We first examine the limitations of traditional …

Rubin's classic missingness mechanisms are central to handling missing data and
minimizing biases that can arise due to missingness. However, the formulaic expressions …

This book provides practical methods for analyzing missing data along with the heuristic
reasoning for understanding the theoretical underpinnings. The author presents both …

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 …

We address the problem of deciding whether there exists a consistent estimator of a given
relation Q, when data are missing not at random. We employ a formal representation …

Missing data has the potential to affect analyses conducted in all fields of scientific study
including healthcare, economics, and the social sciences. Several approaches to unbiased …

Missing data are a recurring problem that can cause bias or lead to inefficient analyses.
Statistical methods to address missingness have been actively pursued in recent years …

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 …

Missing data affect nearly every discipline by complicating the statistical analysis of collected
data. But since the 1990s, there have been important developments in the statistical …

For multisource data, blocks of variable information from certain sources are likely missing.
Existing methods for handling missing data do not take structures of block-wise missing data …