Statistical Analysis with Missing Data
2nd Edition
Published on 9. September 2002
Book
Hardback
408 pages
978-0-471-18386-0 (ISBN)
Description
* Emphasizes the latest trends in the field.
* Includes a new chapter on evolving methods.
* Provides updated or revised material in most of the chapters.
Reviews / Votes
"I enjoyed reading this well written book. I recommend it highly to statisticians." (Journal of Statistical Computation & Simulation, July 2004) "...a well written and well documented text for missing data analysis..." (Statistical Methods in Medical Research, Vol.14, No.1, 2005) "An update to this authoritative book is indeed welcome." (Journal of the American Statistical Association, December 2004) "...this is an excellent book. It is well written and inspiring..." (Statistics in Medicine, 2004; 23) "...this second edition offers a thoroughly up-to-date, reorganized survey of of current methods for handling missing data problems..." (Zentralblatt Math, Vol.1011, No.11, 203) "...well written and very readable...a comprehensive, update treatment of an important topic by two of the leading researchers in the field. In summary, I highly recommend this book..." (Technometrics, Vol. 45, No. 4, November 2003)More details
Series
Edition
2. Auflage
Language
English
Place of publication
New York
United States
Target group
College/higher education
Professional and scholarly
Edition type
New edition
Dimensions
Height: 23.9 cm
Width: 15.8 cm
Thickness: 2.7 cm
Weight
760 gr
ISBN-13
978-0-471-18386-0 (9780471183860)
Schweitzer Classification
Other editions
New editions
Roderick J. A. Little | Donald B. Rubin
Statistical Analysis with Missing Data
Book
05/2019
3rd Edition
Wiley
€98.98
Shipment within 10-20 days
Previous edition
Roderick J. A. Little | Donald B. Rubin
Statistical Analysis with Missing Data
Book
05/1987
99th Edition
Wiley
€100.90
Article exhausted; check for reprint
Persons
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.
Content
Preface.PART 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.Methods Based on Factoring the Likelihood, 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.Models for 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.References.Author Index.Subject Index.