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Statistical analysis with missing data

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Statistical analysis with missing data

Statistical analysis with missing data

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

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 du livre "Statistical analysis with missing data"

  PAPIER
Éditeur(s) Wiley
Auteur(s) Roderick Little, Donald B. Rubin
Parution 10/10/2002
Édition  2eme 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

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