Flexible Imputation of Missing Data (Chapman & Hall/CRC Interdisciplinary Statistics)

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9781439868249: Flexible Imputation of Missing Data (Chapman & Hall/CRC Interdisciplinary Statistics)
Rezension:

"The opening chapters of this book will be useful to the newcomer to missing data, including the nonstatistician. Many of the recommendations in the ‘Do’s and don’ts’ section will be useful to the researcher who encounters missing data and wishes to deal with it responsibly. Finally, the code examples provide a reassuring companion to the user of the mice software package."
Biometrical Journal, 2014

"This book would be well suited as a textbook, especially at the graduate level, possibly for biostatisticians, epidemiologists, or applied scientists and users of statistical methodology. …a very enjoyable read, and—at least in my opinion—it is a book that belongs on everyone’s shelf as it does open one’s eyes to a problem that has surrounded us (and that many of us have ignored!) for a very long time."
—Wolfgang S. Jank, Journal of the American Statistical Association, June 2013

"From the first lines of Chapter 1 throughout the entire monograph, the author presents numerous R language codes, so the book also serves as a good introduction to R. Each chapter is complete with various examples and exercises. The book is very useful to graduate students and researchers for solving practical problems with real data."
Technometrics, February 2013

"It’s excellent and I highly recommend it. … van Buuren’s book is great even if you don’t end up using the algorithm described in the book … he supplies lots of intuition, examples, and graphs."
—Andrew Gelman, Columbia University

"… a beautiful book that is so full of guidance for statisticians … exceptionally up to date and has more useful wisdom about dealing with common missing data problems than any other source I've seen."
—Frank Harrell, Vanderbilt University

"I’m delighted to see this new book on multiple imputation by Stef van Buuren …This book represents a 'no nonsense' straightforward approach to the application of multiple imputation. I particularly like Stef’s use of graphical displays … It’s great to have Stef’s book on multiple imputation, and I look forward to seeing more editions as this rapidly developing methodology continues to become even more effective at handling missing data problems in practice."
—From the Foreword by Donald B. Rubin

Rezension:

"This book would be well suited as a textbook, especially at the graduate level, possibly for biostatisticians, epidemiologists, or applied scientists and users of statistical methodology. …a very enjoyable read, and—at least in my opinion—it is a book that belongs on everyone’s shelf as it does open one’s eyes to a problem that has surrounded us (and that many of us have ignored!) for a very long time."
—Wolfgang S. Jank, Journal of the American Statistical Association, June 2013

"From the first lines of Chapter 1 throughout the entire monograph, the author presents numerous R language codes, so the book also serves as a good introduction to R. Each chapter is complete with various examples and exercises. The book is very useful to graduate students and researchers for solving practical problems with real data."
Technometrics, February 2013

"It’s excellent and I highly recommend it. … van Buuren’s book is great even if you don’t end up using the algorithm described in the book … he supplies lots of intuition, examples, and graphs."
—Andrew Gelman, Columbia University

"… a beautiful book that is so full of guidance for statisticians … exceptionally up to date and has more useful wisdom about dealing with common missing data problems than any other source I've seen."
—Frank Harrell, Vanderbilt University

"I’m delighted to see this new book on multiple imputation by Stef van Buuren …This book represents a 'no nonsense' straightforward approach to the application of multiple imputation. I particularly like Stef’s use of graphical displays … It’s great to have Stef’s book on multiple imputation, and I look forward to seeing more editions as this rapidly developing methodology continues to become even more effective at handling missing data problems in practice."
—From the Foreword by Donald B. Rubin

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