Flexible Imputation of Missing Data

✍ Scribed by Stef van Buuren

• Publisher: Chapman and Hall/CRC
• Year: 2012
• Tongue: English
• Leaves: 326
• Series: Chapman & Hall/CRC Interdisciplinary Statistics
• Edition: 1
• Category: Library

✦ Synopsis

Missing data form a problem in every scientific discipline, yet the techniques required to handle them are complicated and often lacking. One of the great ideas in statistical science—multiple imputation—fills gaps in the data with plausible values, the uncertainty of which is coded in the data itself. It also solves other problems, many of which are missing data problems in disguise.

Flexible Imputation of Missing Data is supported by many examples using real data taken from the author's vast experience of collaborative research, and presents a practical guide for handling missing data under the framework of multiple imputation. Furthermore, detailed guidance of implementation in R using the author’s package MICE is included throughout the book.

Assuming familiarity with basic statistical concepts and multivariate methods, Flexible Imputation of Missing Data is intended for two audiences:

• (Bio)statisticians, epidemiologists, and methodologists in the social and health sciences
• Substantive researchers who do not call themselves statisticians, but who possess the necessary skills to understand the principles and to follow the recipes

This graduate-tested book avoids mathematical and technical details as much as possible: formulas are accompanied by a verbal statement that explains the formula in layperson terms. Readers less concerned with the theoretical underpinnings will be able to pick up the general idea, and technical material is available for those who desire deeper understanding. The analyses can be replicated in R using a dedicated package developed by the author.

✦ Table of Contents

Front Cover......Page 1
Dedication......Page 8
Contents......Page 10
Foreword......Page 18
Preface......Page 20
About the Author......Page 22
Symbol Description......Page 24
List of Algorithms......Page 26
I. Basics......Page 28
1. Introduction......Page 30
2. Multiple imputation......Page 52
3. Univariate missing data......Page 80
4. Multivariate missing data......Page 122
5. Imputation in practice......Page 150
6. Analysis of imputed data......Page 180
II. Case studies......Page 196
7. Measurement issues......Page 198
8. Selection issues......Page 232
9. Longitudinal data......Page 248
III. Extensions......Page 274
10. Conclusion......Page 276
A. Software......Page 290
References......Page 296

✦ Subjects

Информатика и вычислительная техника;Искусственный интеллект;Интеллектуальный анализ данных;