Statistical Methods for Handling Incomplete Data

ISBN-13: 9780367280543 / Angielski / Twarda / 2021 / 364 str.

Jae Kwang Kim; Jun Shao

Opis

Due to recent theoretical findings and advances in statistical computing, there has been a rapid development of techniques and applications in the area of missing data analysis. This book covers the most up-to-date statistical theories and computational methods for analyzing incomplete data.

Recenzje

"As a general comment, I must say that it is probably one of the most extensive, detailed and complete sources of information on the most up-to-date methods to deal with missing data, from simple imputation methods to more complex analysis techniques that take missingness into account. The book is well organized in 12 chapters that although could be read independently based on the readers needs/interest, it does have a hierarchy that makes sense going from more simple early chapters to more complex subjects later in the book."
~David Manteigas, ISCB Book Reviews

Spis treści

1. Introduction
2. Likelihood-based Approach
3. Computation
4. Imputation
5. Multiple Imputation
6. Fractional Imputation
7. Propensity Scoring Approach
8. Nonignorable Missing Data
9. Longitudinal and Clustered Data
10. Application to Survey Sampling
11. Data Integration
12. Advanced Topics