„I enjoyed reading this well written book. I recommend it highly tostatisticians.“ (Journal of Statistical Computation & Simulation, July 2004)
„... a well written and well documented text formissing data analysis...“ (Statistical Methods in MedicalResearch, Vol.14, No.1, 2005)
„An update to this authoritative book is indeed welcome.“(Journal of the American Statistical Association, December2004)
„... this is an excellent book. It is well written andinspiring...“ (Statistics in Medicine, 2004;23)
„... this second edition offers a thoroughly up-to-date, reorganized survey of of current methods for handling missing dataproblems...“ (Zentralblatt Math, Vol.1011, No.11, 203)
„... well written and very readable... a comprehensive, updatetreatment of an important topic by two of the leading researchersin the field. In summary, I highly recommend this book...“(Technometrics, Vol. 45, No. 4, November 2003)
„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.
