Description
Book SynopsisMissing data affect nearly every discipline by complicating the statistical analysis of collected data. But since the 1990s, there have been important developments in the statistical methodology for handling missing data. Written by renowned statisticians in this area, Handbook of Missing Data Methodology presents many methodological advances and the latest applications of missing data methods in empirical research.
Divided into six parts, the handbook begins by establishing notation and terminology. It reviews the general taxonomy of missing data mechanisms and their implications for analysis and offers a historical perspective on early methods for handling missing data. The following three parts cover various inference paradigms when data are missing, including likelihood and Bayesian methods; semi-parametric methods, with particular emphasis on inverse probability weighting; and multiple imputation methods.
The next part of the book foc
Trade Review
"There is evidence of a strong editorial hand—each chapter begins with a table of contents; the notation is surprisingly well standardized for a work by 20 authors; and the number of typos is modest. The chapters refer to each other, but one can read them independently and in any order...This handbook summarizes the authors’ research on a range of missing-data problems of contemporary interest. Methodologists who seek a one-volume entry point into the field will find it useful."
—Journal of the American Statistical Association, May 2016
"There is evidence of a strong editorial hand—each chapter begins with a table of contents; the notation is surprisingly well standardized for a work by 20 authors; and the number of typos is modest. The chapters refer to each other, but one can read them independently and in any order...This handbook summarizes the authors’ research on a range of missing-data problems of contemporary interest. Methodologists who seek a one-volume entry point into the field will find it useful."
—Journal of the American Statistical Association
Table of ContentsPreliminaries. Likelihood and Bayesian Methods, Semiparametric Methods. Multiple Imputation. Sensitivity Analysis. Special Topics. Index.
