Statistical Analysis with Missing Data

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9780471183860: Statistical Analysis with Missing Data
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Praise for the First Edition of Statistical Analysis with MissingData

"An important contribution to the applied statisticsliterature.... I give the book high marks for unifying and makingaccessible much of the past and current work in this importantarea."
—William E. Strawderman, Rutgers University

"This book...provide[s] interesting real-life examples,stimulating end-of-chapter exercises, and up-to-date references. Itshould be on every applied statistician’s bookshelf."
The Statistician

"The book should be studied in the statistical methodsdepartment in every statistical agency."
Journal of Official Statistics

Statistical analysis of data sets with missing values is apervasive problem for which standard methods are of limited value.The first edition of Statistical Analysis with Missing Data hasbeen a standard reference on missing-data methods. Now, reflectingextensive developments in Bayesian methods for simulating posteriordistributions, this Second Edition by two acknowledged experts onthe subject offers a thoroughly up-to-date, reorganized survey ofcurrent methodology for handling missing-data problems.

Blending theory and application, authors Roderick Little andDonald Rubin review historical approaches to the subject anddescribe rigorous yet simple methods for multivariate analysis withmissing values. They then provide a coherent theory for analysis ofproblems based on likelihoods derived from statistical models forthe data and the missing-data mechanism and apply the theory to awide range of important missing-data problems.

The new edition now enlarges its coverage to include:

  • Expanded coverage of Bayesian methodology, both theoretical andcomputational, and of multiple imputation
  • Analysis of data with missing values where inferences are basedon likelihoods derived from formal statistical models for thedata-generating and missing-data mechanisms
  • Applications of the approach in a variety of contexts includingregression, factor analysis, contingency table analysis, timeseries, and sample survey inference
  • Extensive references, examples, and exercises

Amstat News asked three review editors to rate their topfive favorite books in the September 2003 issue. StatisticalAnalysis With Missing Data was among those chosen.

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From the Publisher:

Acknowledged experts on the subject bring together diverse sources on methods for statistical analysis of data sets with missing values, a pervasive problem for which standard methods are of limited value. Blending theory and application, it reviews historical approaches to the subject, and rigorous yet simple methods for multivariate analysis with missing values. Goes on to provide a coherent theory for analysis of problems based on likelihoods derived from statistical models for the data and the missing data mechanism. The theory is applied to a wide range of important missing-data problems. Extensive references, examples, and exercises.

About the Author:

RODERICK J. A. LITTLE, PhD, is Professor and Chair of Biostatistics at the University of Michigan.

DONALD B. RUBIN, PhD, is the Chair of the Department of Statistics at Harvard University.

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9780471802549: Statistical Analysis With Missing Data (Wiley Series in Probability and Statistics)

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