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Missing data and small-area estimation. Modern analytical equipment for the survey statistician. (English) Zbl 1092.62008
Statistics for Social Science and Public Policy. London: Springer (ISBN 1-85233-760-5/hbk). xv, 357 p. (2005).
This book develops methods for two key problems in the analysis of large-scale surveys dealing with incomplete data and making inferences about sparsely represented subdomains. The presentation is committed to two particular methods, multiple imputation for missing data and multivariate composition for small-area estimation. The methods are presented as developments of established approaches by attending to their deficiencies. Thus the change to more efficient methods can be gradual, sensitive to the management priorities in large research organisations and multidisciplinary teams and to other reasons for inertia. The typical setting of each problem is addressed first, and then the constituency of the applications is widened to reinforce the view that the general method is essential for modern survey analysis. The general tone of the book is not “from theory to practice” but “from current practice to better practice”.
The first part is devoted to “Missing data” and contains: 1. Prologue; 2. Describing incompleteness; 3. Single imputation and related methods; 4. Multiple imputation; 5. Case studies. The second part deals with “Small-area estimation” containing: 6. Introduction; 7. Models for small areas; 8. Using auxilliary information; 9. Using small-area estimators; 10. Case studies. The third part, a single chapter “Model selection” presents a method for efficient estimation under model uncertainty. It is inspired by the solution for small-area estimation and is an example of “from good practice to better theory”.
A strength of the presentation are the chapters on case studies, one for each problem. Whenever possible, turning to examples and illustrations is preferred to theoretical arguments. The book is suitable for graduate students and researchers who are acquainted with the fundamentals of sampling theory and have a good grounding in statistical computing, or in conjunction with an intensive period of learning and establishing one’s own modern computing and graphical environment that would serve the reader for most of the analytical work in the future. This book presents also opportunities to harness the computing power into service of high-quality socially relevant statistics.

MSC:
62D05 Sampling theory, sample surveys
62P25 Applications of statistics to social sciences
62-02 Research exposition (monographs, survey articles) pertaining to statistics
65C60 Computational problems in statistics (MSC2010)
Software:
R
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