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Small area estimation: A Bayesian perspective. (English) Zbl 0956.62020
Ghosh, Subir (ed.), Multivariate analysis, design of experiments, and survey sampling. A tribute to Jagdish N. Srivastava. New York, NY: Marcel Dekker. Stat., Textb. Monogr. 159, 69-92 (1999).
From the introduction: Small area estimation is becoming a topic of growing importance in recent years. The terms “small area” and “local area” are commonly used to denote a small geographical area such as a county, municipality, or a census division. They may also describe a “small domain,” that is a small subpopulation such as a specific age-sex-race group of people in a large geographical area.
In Section 2 of this review we discuss some synthetic and composite estimators, and Bayesian interpretations of the same. In Section 3, we discuss some specific simple normal models that have been used in the context of small area estimation. Bayesian inference based on hierarchical Bayes generalized linear models is discussed in Section 4. Finally, Section 5 introduces Bayesian model averaging in the context of small area estimation, and this idea is illustrated with an example.
For the entire collection see [Zbl 0927.00053].
MSC:
62F15 Bayesian inference
62J12 Generalized linear models (logistic models)
62M30 Inference from spatial processes
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