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 methodology for
handling missing data. Written by renowned statisticians in this
area, Handbook of Missing Data Methodology presents many
methodological advances and the latest applications of missing data
methods in empirical research. Divided into six parts, the handbook
begins by establishing notation and terminology. It reviews the
general taxonomy of missing data mechanisms and their implications
for analysis and offers a historical perspective on early methods
for handling missing data. The following three parts cover various
inference paradigms when data are missing, including likelihood and
Bayesian methods; semi-parametric methods, with particular emphasis
on inverse probability weighting; and multiple imputation methods.
The next part of the book focuses on a range of approaches that
assess the sensitivity of inferences to alternative, routinely
non-verifiable assumptions about the missing data process. The
final part discusses special topics, such as missing data in
clinical trials and sample surveys as well as approaches to model
diagnostics in the missing data setting. In each part, an
introduction provides useful background material and an overview to
set the stage for subsequent chapters. Covering both established
and emerging methodologies for missing data, this book sets the
scene for future research. It provides the framework for readers to
delve into research and practical applications of missing data
Chapman & Hall/CRC
|Country of origin:
||Chapman & Hall/CRC Handbooks of Modern Statistical Methods
• Garrett Fitzmaurice
• Michael G. Kenward
• Anastasios A. Tsiatis
• Geert Verbeke
||Electronic book text
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