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**Statistical analysis with missing data.
2nd ed.**
*(English)*
Zbl 1011.62004

Wiley Series in Probability and Statistics. Chichester: Wiley. xviii, 381 p. (2002).

Statistical analysis of data sets with missing values is a pervasive problem for which standard methods are of limited value. The first edition of this book from 1987 has been for a long time a standard reference on missing data methods. Reflecting extensive developments in Bayesian methods for simulating posterior distributions, this second edition offers a thoroughly up-to-date, reorganized survey of current methods for handling missing data problems.

Blending theory and applications, the authors review historical approaches to the subject and describe rigorous yet simple methods for multivariate analysis with missing data. They then provide a coherent theory for analysis of problems based on likelihoods derived from statistical models for the data and the missing data mechanism and apply the theory to a wide range of important missing data problems. The new edition now enlarges its coverage to include:

\(\bullet\) Expanded coverage of Bayesian methodology, both theoretical and computational, and of multiple imputation. \(\bullet\) Analysis of data with missing values where inferences are based on likelihoods derived from formal statistical models for the data generating and missing data mechanism. \(\bullet\) Application of the approach in a variety of contexts including regression, factor analysis, contingency table analysis, time series, and sample survey inference. \(\bullet\) Extensive references, examples, and exercises.

Contents: 1. Introduction; 2. Missing data in experiments; 3. Complete-case and available-case analysis, including weighting methods; 4. Single imputation methods; 5. Estimation of imputation uncertainty; 6. Theory of inference based on the likelihood function; 7. Factor likelihood methods, ignoring the missing data mechanism; 8. Maximum likelihood for general patterns of missing data; 9. Large sample inference based on maximum likelihood estimates; 10. Bayes and multiple imputations; 11. Multivariate normal examples, ignoring the missing data mechanism; 12. Robust estimation; 13. Models for partially classified contingency tables, ignoring the missing data mechanism; 14. Mixed normal and non-normal data with missing values, ignoring the missing data mechanism; 15. Nonignorable missing data models. References, Author index and Subject index.

Blending theory and applications, the authors review historical approaches to the subject and describe rigorous yet simple methods for multivariate analysis with missing data. They then provide a coherent theory for analysis of problems based on likelihoods derived from statistical models for the data and the missing data mechanism and apply the theory to a wide range of important missing data problems. The new edition now enlarges its coverage to include:

\(\bullet\) Expanded coverage of Bayesian methodology, both theoretical and computational, and of multiple imputation. \(\bullet\) Analysis of data with missing values where inferences are based on likelihoods derived from formal statistical models for the data generating and missing data mechanism. \(\bullet\) Application of the approach in a variety of contexts including regression, factor analysis, contingency table analysis, time series, and sample survey inference. \(\bullet\) Extensive references, examples, and exercises.

Contents: 1. Introduction; 2. Missing data in experiments; 3. Complete-case and available-case analysis, including weighting methods; 4. Single imputation methods; 5. Estimation of imputation uncertainty; 6. Theory of inference based on the likelihood function; 7. Factor likelihood methods, ignoring the missing data mechanism; 8. Maximum likelihood for general patterns of missing data; 9. Large sample inference based on maximum likelihood estimates; 10. Bayes and multiple imputations; 11. Multivariate normal examples, ignoring the missing data mechanism; 12. Robust estimation; 13. Models for partially classified contingency tables, ignoring the missing data mechanism; 14. Mixed normal and non-normal data with missing values, ignoring the missing data mechanism; 15. Nonignorable missing data models. References, Author index and Subject index.

Reviewer: JaromĂr Antoch (Praha)

### MSC:

62-01 | Introductory exposition (textbooks, tutorial papers, etc.) pertaining to statistics |

62F10 | Point estimation |

62N01 | Censored data models |

62H17 | Contingency tables |

62D05 | Sampling theory, sample surveys |