Flexible Imputation of Missing Data

Stef van Buuren

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March 29, 2012 by Chapman and Hall/CRC
Professional - 342 Pages - 58 B/W Illustrations
ISBN 9781439868249 - CAT# K13103
Series: Chapman & Hall/CRC Interdisciplinary Statistics

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Features

  • Provides an accessible introduction to multiple imputation for handling missing data
  • Examines various missing-data problems and presents strategies for tackling them
  • Includes many examples using real data
  • Supported by an R package, enabling the reader to replicate the analyses and use the methods in their own work.
  • All material downloadable from www.multiple-imputation.com

Summary

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.