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Missing Data Methodology

Handbooks of Modern Statistical Methods

Edited by: Molenberghs Geert , Fitzmaurice Garrett , G. Kenward Michael , Tsiatis Anastasios , Verbeke Geert

Print publication date:  November  2014
Online publication date:  November  2014

Print ISBN: 9781439854617
eBook ISBN: 9781439854624
Adobe ISBN:

10.1201/9781439854624
 Cite  Marc Record

Book description

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 methods.

Table of contents

Chapter  1:  Missing Data Download PDF
Chapter  2:  Development of Methods for the Analysis of Partially Observed Data and Critique of ad hoc Methods Download PDF
Chapter  3:  Likelihood and Bayesian Methods Download PDF
Chapter  4:  A Perspective and Historical Overview on Selection, Pattern-Mixture and Shared Parameter Models Download PDF
Chapter  5:  Bayesian Methods for Incomplete Data Download PDF
Chapter  6:  Joint Modeling of Longitudinal and Time-to-Event Data Download PDF
Chapter  7:  Semiparametric Methods: Introduction and Overview Download PDF
Chapter  8:  Missing Data Methods Download PDF
Chapter  9:  Double-Robust Methods Download PDF
Chapter  10:  Pseudo-Likelihood Methods for Incomplete Data Download PDF
Chapter  11:  Introduction Download PDF
Chapter  12:  Multiple Imputation: A Perspective and Historical Overview Download PDF
Chapter  13:  Fully Conditional Specification Download PDF
Chapter  14:  Multilevel Multiple Imputation Download PDF
Chapter  15:  Sensitivity Analysis: Introduction and Overview Download PDF
Chapter  16:  A Likelihood-Based Perspective on Sensitivity Analysis Download PDF
Chapter  17:  Sensitivity Analysis: A Semi-Parametric Perspective Download PDF
Chapter  18:  A Bayesian Perspective on Assessing Sensitivity to Assumptions about Unobserved Data Download PDF
Chapter  19:  Sensitivity Analysis with Multiple Imputation Download PDF
Chapter  20:  Sensitivity Analysis: The Elicitation and Use of Expert Opinion Download PDF
Chapter  21:  Special Topics: Introduction and Overview Download PDF
Chapter  22:  Missing Data in Clinical Trials Download PDF
Chapter  23:  Missing Data in Sample Surveys Download PDF
Chapter  24:  Model Diagnostics Download PDF
prelims Download PDF
Index Download PDF
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