Free Download Statistical Analysis with Missing Data (Wiley Series in Probability and Statistics)
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Statistical Analysis with Missing Data (Wiley Series in Probability and Statistics)
Free Download Statistical Analysis with Missing Data (Wiley Series in Probability and Statistics)
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About the Author
Roderick J. A. Little, PhD., is Richard D. Remington Distinguished University Professor of Biostatistics, Professor of Statistics, and Research Professor, Institute for Social Research, at the University of Michigan. Donald B. Rubin, PhD., is Professor, Yau Mathematical Sciences Center, Tsinghua University; Murray Shusterman Senior Research Fellow, Department of Statistical Science, Fox School of Business at Temple University; and Professor Emeritus, Harvard University.
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Product details
Series: Wiley Series in Probability and Statistics (Book 793)
Hardcover: 464 pages
Publisher: Wiley; 3 edition (May 14, 2019)
Language: English
ISBN-10: 0470526793
ISBN-13: 978-0470526798
Shipping Weight: 1.7 pounds (View shipping rates and policies)
Average Customer Review:
4.5 out of 5 stars
4 customer reviews
Amazon Best Sellers Rank:
#772,920 in Books (See Top 100 in Books)
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.
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.
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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