MULTIPLE IMPUTATION OF MISSING DATA IN
HE YULEI
Sold by Speedyhen, Hertfordshire, United Kingdom
AbeBooks Seller since 26 November 2009
New - Soft cover
Condition: New
Ships from United Kingdom to U.S.A.
Quantity: 2 available
Add to basketSold by Speedyhen, Hertfordshire, United Kingdom
AbeBooks Seller since 26 November 2009
Condition: New
Quantity: 2 available
Add to basketMultiple Imputation of Missing Data in Practice: Basic Theory and Analysis Strategies provides a comprehensive introduction to the multiple imputation approach to missing data problems that are often encountered in data analysis. Over the past 40 years or so, multiple imputation has gone through rapid development in both theories and applications. It is nowadays the most versatile, popular, and effective missing-data strategy that is used by researchers and practitioners across different fields. There is a strong need to better understand and learn about multiple imputation in the research and practical community.
Accessible to a broad audience, this book explains statistical concepts of missing data problems and the associated terminology. It focuses on how to address missing data problems using multiple imputation. It describes the basic theory behind multiple imputation and many commonly-used models and methods. These ideas are illustrated by examples from a wide variety of missing data problems. Real data from studies with different designs and features (e.g., cross-sectional data, longitudinal data, complex surveys, survival data, studies subject to measurement error, etc.) are used to demonstrate the methods. In order for readers not only to know how to use the methods, but understand why multiple imputation works and how to choose appropriate methods, simulation studies are used to assess the performance of the multiple imputation methods. Example datasets and sample programming code are either included in the book or available at a github site (https://github.com/he-zhang-hsu/multiple_imputation_book).
Key Features
Yulei He and Guangyu Zhang are mathematical statisticians at the National Center for Health Statistics, the U.S. Centers for Disease Control and Prevention. Chiu-Heish Hsu is a Professor of Biostatistics at the University of Arizona. All authors have researched, taught, and consulted in multiple imputation and missing data analysis in the past 20 years.
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