Statistical Analysis with Missing Data (Wiley Series in Probability and Statistics Book 793) 3rd Edition by Roderick J. A. Little (PDF)

Description

An up-to-date, comprehensive treatment of a classic text on missing data in statisticsThe topic of missing data has gained considerable attention in recent decades. This new edition by two acknowledged experts on the subject offers an up-to-date account of practical methodology for handling missing data problems. Blending theory and application, authors Roderick Little and Donald Rubin review historical approaches to the subject and describe 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 then they apply the theory to a wide range of important missing data problems.Statistical Analysis with Missing Data, Third Edition starts by introducing readers to the subject and approaches toward solving it. It looks at the patterns and mechanisms that create the missing data, as well as a taxonomy of missing data. It then goes on to examine missing data in experiments, before discussing complete-case and available-case analysis, including weighting methods. The new edition expands its coverage to include recent work on topics such as nonresponse in sample surveys, causal inference, diagnostic methods, and sensitivity analysis, among a host of other topics. An updated “classic” written by renowned authorities on the subjectFeatures over 150 exercises (including many new ones)Covers recent work on important methods like multiple imputation, robust alternatives to weighting, and Bayesian methodsRevises previous topics based on past student feedback and class experienceContains an updated and expanded bibliography The authors were awarded The Karl Pearson Prize in 2017 by the International Statistical Institute, for a research contribution that has had profound influence on statistical theory, methodology or applications. Their work “has been no less than defining and transforming.” (ISI)Statistical Analysis with Missing Data, Third Edition is an ideal textbook for upper undergraduate and/or beginning graduate level students of the subject. It is also an excellent source of information for applied statisticians and practitioners in government and industry.

User’s Reviews

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐I’m working with data sets where up to 15% of measurements are unusable. If I’m going to get any results at all, I have to get them despite the lost values.This book provides a huge library of techniques for working around the holes, as well as techniques for filling them in. This is not a cut-and-paste text for programmers – it gives the basic theory and algorithms for each technique. Still, the presentation is quite readable and fairly easy to put into practice.The book’s emphasis is on imputation – filling in values so that analysis can move forward. This is something to approach with real caution, though. The imputed (synthesized) values must not perturb the analysis, so the imputation must differ according to the analysis being performed. The authors present a variety of imputation techniques, as well as bootstrap, jacknife, and other techniques for measuring the quality of the results.The authors also dedicate chapters to approaches that work only with available data, and to cases where missing data can not simply be ignored.This is the most thorough and practical guide I know to handling missing data. In an ideal world, experiments would all produce usable results and surveys would all have every question answered. When you have to deal with reality, though, this is the book.

⭐A good text. It was required for a graduate level course on handling missing data. The professor has also opted to use a portion of an additional textbook with some more modern techniques not addressed in this book such as pattern mixture models. But this is a great intro to the topic. I will say, however, some of the problems are vague and poorly explained. Computationally they are not too difficult, but trying to figure out exactly what they are asking is sometimes tough.

⭐This is a classic and should be part of your library if you are a serious statistician.

⭐I have previously given great praise to this book under the pen name of statman13. To add to my previous reviews I should say that Little and Rubin continue to be the top researchers in this field and Don Rubin often consults with the FDA, the pharmaceutical industry and statistical review boards. He is an eloquent speaker and writer as is also his co-author Rod Little. The development of the model classifications MAR, MCAR and MNAR (or nonignorable missingness)is due to Rubin and is quite common these days in the thinking of researchers involved with missing data in their analyses. In the cases where the missing mechanism is not ignorable pattern mixture models, that Little had a major role in developing, are given. All this wonderful work is spelled out in this book. This second edition has added much discussion of Bayesian methods using the current computational advantages of Gibbs sampling. Also some specific techniques have software implementation in SAS or SPlus and this is pointe dout by the authors when it comes up. I think that rather than searching through the index to find where sofware is mentioned it would be nice to have a section of the book devoted to it. oddly the software tool SOLAS that Rubin had a part in aiding the development does not appear to be mentioned in the book. Perhaps the authors will expand upon the discussion of software in the next edition. Also new to this edition is more detailed coverage of multiple imputation. Resampling techniques are also discussed in the context of getting sensible estimates of the standard deviation of the estimated parameters in the face of imputing some of the data.

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