Bayesian Methods for Finite Population Sampling

Name: Bayesian Methods for Finite Population Sampling
Brand: Chapman and Hall
SKU: 9780412987717
Price: 2157 CZK
Availability: InStock

Autor: Malay Ghosh, G. Meeden

Jazyk:

Vazba: Brožovaná

Vydavatel: Chapman and Hall

Dostupnost: U nakladatele na objednávku

Odesíláme za 17-27 dnů

2 157 Kč

The present monograph is primarily an outgrowth of our own re search on certain aspects of Bayesian...

Informace o knize

Autor

Malay Ghosh, G. Meeden

Jazyk

Angličtina

Vazba

Kniha - Brožovaná

Vydáno

1997

Stránek

296

EAN

9780412987717

ISBN

0412987716

Enbook ID

02636126

Vydavatel

Chapman and Hall

Hmotnost

424

Rozměry

145 x 215 x 20

Kompletní popis

The present monograph is primarily an outgrowth of our own re search on certain aspects of Bayesian inference in finite population sampling. Finite population sampling has been an integral part of statistics since its beginning. The topic continues its impact in the theory and practice of statistics, especially for researchers in survey sampling. Inference for finite population sampling utilizes prior information either explicitly or implicitly. Bayesian inference makes explicit use of this information as part of the model. This is in striking con trast to design- based inference in survey sampling where prior knowledge is incorporated only as auxiliary information. On the other hand there is a elose relationship between the Bayesian ap proach and the superpopulation approach, although they differ in their foundational interpretations. Operationally, however, the dif ference is much less pronounced as many estimators obtained via superpopulation models are also obtainable as Bayes estimators, and vice versa. This monograph, does not aim to provide a complete up-to-date account of the Bayesian literature in finite population sampling. Rather, it treats the topics reflecting the authors' personal inter ests. Its main aim is to demonstrate that a variety of levels of prior information can be used in survey sampling in a Bayesian man ner. Situations considered range from a noninformative Bayesian justification of standard frequentist methods when the only prior information available is the belief in the exchangeablility of the units to a full-fledged Bayesian model.

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