Title Details

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

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.

“…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)