Statistical analysis with missing data

Roderick Little, Donald B. Rubin

396 pages, parution le 10/10/2002 (2^eme édition)

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Résumé

Statistical analysis of data sets with missing values is a pervasive problem for which standard methods are of limited value. The first edition of Statistical Analysis with Missing Data has been a standard reference on missing-data methods. Now, reflecting extensive developments in Bayesian methods for simulating posterior distributions, this Second Edition by two acknowledged experts on the subject offers a thoroughly up-to-date, reorganized survey of current methodology for handling missing-data problems. Blending theory and application, authors Roderick Little and Donald Rubin review historical approaches to the subject and describe rigorous yet simple methods for multivariate analysis with missing values. They then provide a coherent theory for analysis of problems based on likelihoods derived from statistical models for the data and the missing-data mechanism and apply the theory to a wide range of important missing-data problems. The new edition now enlarges its coverage to include:

Expanded coverage of Bayesian methodology, both theoretical and computational, and of multiple imputation
Analysis of data with missing values where inferences are based on likelihoods derived from formal statistical models for the data-generating and missing-data mechanisms
Applications of the approach in a variety of contexts including regression, factor analysis, contingency table analysis, time series, and sample survey inference
Extensive references, examples, and exercises

ContentsPART I: OVERVIEW AND BASIC APPROACHES.

Introduction
Missing Data in Experiments
Complete-Case and Available-Case Analysis, Including Weighting Methods
Single Imputation Methods
Estimation of Imputation Uncertainty

PART II: LIKELIHOOD-BASED APPROACHES TO THE ANALYSIS OF MISSING DATA.

Theory of Inference Based on the Likelihood Function
Factored likelihood methods, ignoring the missing-data mechanism
Maximum Likelihood for General Patterns of Missing Data: Introduction and Theory with Ignorable Nonresponse
Large-Sample Inference Based on Maximum Likelihood Estimates
Bayes and Multiple Imputation

PART III: LIKELIHOOD-BASED APPROACHES TO THE ANALYSIS OF MISSING DATA: APPLICATIONS TO SOME COMMON MODELS

Multivariate Normal Examples, Ignoring the Missing-Data Mechanism
Robust Estimation
Models for Partially Classified Contingency Tables, Ignoring the Missing-Data Mechanism
Mixed Normal and Nonnormal Data with Missing Values, Ignoring the Missing-Data Mechanism
Nonignorable Missing-Data Models

L'auteur - Donald B. Rubin

Donald B. Rubin , PhD, is a John L. Loeb Professor of Statistics at Harvard University in Cambridge, MA. He was named 2000-2001 Statistician of the Year by the Chicago Chapter of ASA. His research interests include causal inference in experiments and observational studies, developing and applying statistical models to data in a variety of scientific disciplines, and the application of Bayesian and empirical Bayesian techniques.

Caractéristiques techniques

	PAPIER
Éditeur(s)	Wiley
Auteur(s)	Roderick Little, Donald B. Rubin
Parution	10/10/2002
Édition	2^eme édition
Nb. de pages	396
Format	16 x 24
Couverture	Relié
Poids	695g
Intérieur	Noir et Blanc
EAN13	9780471183860
ISBN13	978-0-471-18386-0