Handbook of Statistics_29B: Sample Surveys: Inference and Analysis (Google eBook)

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Morgan Kaufmann, Sep 2, 2009 - Mathematics - 666 pages
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This new handbook contains the most comprehensive account of sample surveys theory and practice to date. It is a second volume on sample surveys, with the goal of updating and extending the sampling volume published as volume 6 of the Handbook of Statistics in 1988. The present handbook is divided into two volumes (29A and 29B), with a total of 41 chapters, covering current developments in almost every aspect of sample surveys, with references to important contributions and available software. It can serve as a self contained guide to researchers and practitioners, with appropriate balance between theory and real life applications.

Each of the two volumes is divided into three parts, with each part preceded by an introduction, summarizing the main developments in the areas covered in that part. Volume 1 deals with methods of sample selection and data processing, with the later including editing and imputation, handling of outliers and measurement errors, and methods of disclosure control. The volume contains also a large variety of applications in specialized areas such as household and business surveys, marketing research, opinion polls and censuses. Volume 2 is concerned with inference, distinguishing between design-based and model-based methods and focusing on specific problems such as small area estimation, analysis of longitudinal data, categorical data analysis and inference on distribution functions. The volume contains also chapters dealing with case-control studies, asymptotic properties of estimators and decision theoretic aspects.

Comprehensive account of recent developments in sample survey theory and practice

Covers a wide variety of diverse applications

Comprehensive bibliography


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Chapter 1 Foundations
Chapter 2 Computational Intelligence
Chapter 3 Evolutionary Computation Concepts and Paradigms
Chapter 4 Evolutionary Computation Implementations
Chapter 5 Neural Network Concepts and Paradigms
Chapter 6 Neural Network Implementations
Chapter 7 Fuzzy Systems Conceptsand Paradigms
Chapter 8 Fuzzy Systems Implementations
Chapter 10 Performance Metrics
Chapter 11 Analysis and Explanation
About the Authors
Chapter 12 Case Study Summaries

Chapter 9 Computational Intelligence Implementations

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