Admissible estimators in finite population sampling employing various types of prior information

Stephen Vardeman; Glen Meeden

doi:10.1016/0378-3758(83)90004-6

Admissible estimators in finite population sampling employing various types of prior information

Stephen Vardeman, Glen Meeden

Statistics (Twin Cities)

Research output: Contribution to journal › Article › peer-review

5 Scopus citations

Abstract

We consider some estimators of the total and variance of a finite population from Bayesian and pseudo-Bayesian perspectives. Recently, Meeden and Ghosh (1982a, 1982b) have provided quite simple but powerful tools for proving admissibility of estimators and estimator-design pairs is finite population sampling problems. We consider what these techniques yield in the way of admissibility results for the estimators discussed.

Original language	English (US)
Pages (from-to)	329-341
Number of pages	13
Journal	Journal of Statistical Planning and Inference
Volume	7
Issue number	4
DOIs	https://doi.org/10.1016/0378-3758(83)90004-6
State	Published - 1983

Bibliographical note

Funding Information:
author’s research was supported by NSF Grant Number MCS-8005485.

Keywords

Admissibility
Basu estimator
Bayes
Difference estimator
Dirichlet process
Finite population sampling
Uniform admissibility

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10.1016/0378-3758(83)90004-6

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title = "Admissible estimators in finite population sampling employing various types of prior information",

abstract = "We consider some estimators of the total and variance of a finite population from Bayesian and pseudo-Bayesian perspectives. Recently, Meeden and Ghosh (1982a, 1982b) have provided quite simple but powerful tools for proving admissibility of estimators and estimator-design pairs is finite population sampling problems. We consider what these techniques yield in the way of admissibility results for the estimators discussed.",

keywords = "Admissibility, Basu estimator, Bayes, Difference estimator, Dirichlet process, Finite population sampling, Uniform admissibility",

author = "Stephen Vardeman and Glen Meeden",

note = "Funding Information: author{\textquoteright}s research was supported by NSF Grant Number MCS-8005485.",

year = "1983",

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T1 - Admissible estimators in finite population sampling employing various types of prior information

AU - Vardeman, Stephen

AU - Meeden, Glen

N1 - Funding Information: author’s research was supported by NSF Grant Number MCS-8005485.

PY - 1983

Y1 - 1983

N2 - We consider some estimators of the total and variance of a finite population from Bayesian and pseudo-Bayesian perspectives. Recently, Meeden and Ghosh (1982a, 1982b) have provided quite simple but powerful tools for proving admissibility of estimators and estimator-design pairs is finite population sampling problems. We consider what these techniques yield in the way of admissibility results for the estimators discussed.

AB - We consider some estimators of the total and variance of a finite population from Bayesian and pseudo-Bayesian perspectives. Recently, Meeden and Ghosh (1982a, 1982b) have provided quite simple but powerful tools for proving admissibility of estimators and estimator-design pairs is finite population sampling problems. We consider what these techniques yield in the way of admissibility results for the estimators discussed.

KW - Admissibility

KW - Basu estimator

KW - Bayes

KW - Difference estimator

KW - Dirichlet process

KW - Finite population sampling

KW - Uniform admissibility

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SP - 329

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JO - Journal of Statistical Planning and Inference

JF - Journal of Statistical Planning and Inference

IS - 4

ER -

Admissible estimators in finite population sampling employing various types of prior information

Abstract

Bibliographical note

Keywords

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