Flexible Imputation of Missing Data

by Stef van Buuren

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 This graduate-tested book avoids mathematical and technical details as much as 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.

342 pages, Hardcover

First published January 1, 2012

Reviews

[Wej · Rating 3 out of 5]

This book extensively covers a rather dry, yet important topic of data imputation. It provides examples and explanations of imputations in notation and code. The code is written in R and is focused on several techniques of imputation, with multiple imputation using the author's mice package. It wasn't an easy read and I skipped some chapters but this is a crucial textbook for working with missing data. The book was made freely available online.