Missing Data Analysis in Practice
Language: English
Published by CRC Press, 2020
Series: Book 25 of 45 - Chapman & Hall/CRC Interdisciplinary Statistics
- Softcover
- New
Condition: New
£ 68.95
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Trivellore Raghunathan is the director of the Survey Research Center in the Institute for Social Research and professor of biostatistics in the School of Public Health at the University of Michigan. He has published numerous papers in a .
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- Title
- Missing Data Analysis in Practice
- Author
- Trivellore Raghunathan (University of Michigan, Institute of Social Research, Ann Arbor, USA)
- Publisher
- CRC Press
- Publication year
- 2020
- Condition
- New
- Binding
- Einband - flex.(Paperback)
- Language
- English
- ISBN 10
- 0367737663
- ISBN 13
- 9780367737665
- Series
- Book 25 of 45: Chapman & Hall/CRC Interdisciplinary Statistics
Missing Data Analysis in Practice provides practical methods for analyzing missing data along with the heuristic reasoning for understanding the theoretical underpinnings. Drawing on his 25 years of experience researching, teaching, and consulting in quantitative areas, the author presents both frequentist and Bayesian perspectives. He describes easy-to-implement approaches, the underlying assumptions, and practical means for assessing these assumptions. Actual and simulated data sets illustrate important concepts, with the data sets and codes available online.
The book underscores the development of missing data methods and their adaptation to practical problems. It mainly focuses on the traditional missing data problem. The author also shows how to use the missing data framework in many other statistical problems, such as measurement error, finite population inference, disclosure limitation, combing information from multiple data sources, and causal inference.
"Synopsis" may belong to another edition of this title.
About the Author
Trivellore Raghunathan is the director of the Survey Research Center in the Institute for Social Research and professor of biostatistics in the School of Public Health at the University of Michigan. He has published numerous papers in a range of statistical and public health journals. His research interests include applied regression analysis, linear models, design of experiments, sample survey methods, and Bayesian inference.
"About the title" may belong to another edition of this title.
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