Estimating the Variance of a Normal Population by Utilizing the Information in a Sample from a Second Related Normal

Mohammad Fraiwan Al-Saleh; Hani Samawi

doi:10.1080/0094965031000104323

Estimating the Variance of a Normal Population by Utilizing the Information in a Sample from a Second Related Normal

Mohammad Fraiwan Al-Saleh, Hani Samawi

Biostatistics, Epidemiology, & Environmental Health Sciences

Yarmouk University

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

1 Scopus citations

Abstract

A class of estimators of the variance σ1 2 of a normal population is introduced, by utilization the information in a sample from a second normal population with different mean and variance σ2 2, under the restriction that σ1 2 ≤ σ2 2. Simulation results indicate that some members of this class are more efficient than the usual minimum variance unbiased estimator (MVUE) of σ1 2, Stein estimator and Mehta and Gurland estimator. The case of known and unknown means are considered.

Original language	American English
Journal	Journal of Statistical Computation and Simulation
Volume	74
DOIs	https://doi.org/10.1080/0094965031000104323
State	Published - 2004

Keywords

Bayesian estimation
Generalized Bayes
Mean squared error
Mehta and Gurland estimator
Normal population
Posterior
Prior
Stein estimator

DC Disciplines

Biostatistics

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10.1080/0094965031000104323License: Unspecified

Cite this

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title = "Estimating the Variance of a Normal Population by Utilizing the Information in a Sample from a Second Related Normal",

abstract = " A class of estimators of the variance σ1 2 of a normal population is introduced, by utilization the information in a sample from a second normal population with different mean and variance σ2 2, under the restriction that σ1 2 ≤ σ2 2. Simulation results indicate that some members of this class are more efficient than the usual minimum variance unbiased estimator (MVUE) of σ1 2, Stein estimator and Mehta and Gurland estimator. The case of known and unknown means are considered.",

keywords = "Bayesian estimation, Generalized Bayes, Mean squared error, Mehta and Gurland estimator, Normal population, Posterior, Prior, Stein estimator",

author = "Al-Saleh, {Mohammad Fraiwan} and Hani Samawi",

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doi = "10.1080/0094965031000104323",

language = "American English",

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journal = "Journal of Statistical Computation and Simulation",

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T1 - Estimating the Variance of a Normal Population by Utilizing the Information in a Sample from a Second Related Normal

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AU - Samawi, Hani

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N2 - A class of estimators of the variance σ1 2 of a normal population is introduced, by utilization the information in a sample from a second normal population with different mean and variance σ2 2, under the restriction that σ1 2 ≤ σ2 2. Simulation results indicate that some members of this class are more efficient than the usual minimum variance unbiased estimator (MVUE) of σ1 2, Stein estimator and Mehta and Gurland estimator. The case of known and unknown means are considered.

AB - A class of estimators of the variance σ1 2 of a normal population is introduced, by utilization the information in a sample from a second normal population with different mean and variance σ2 2, under the restriction that σ1 2 ≤ σ2 2. Simulation results indicate that some members of this class are more efficient than the usual minimum variance unbiased estimator (MVUE) of σ1 2, Stein estimator and Mehta and Gurland estimator. The case of known and unknown means are considered.

KW - Bayesian estimation

KW - Generalized Bayes

KW - Mean squared error

KW - Mehta and Gurland estimator

KW - Normal population

KW - Posterior

KW - Prior

KW - Stein estimator

UR - https://doi.org/10.1080/0094965031000104323

U2 - 10.1080/0094965031000104323

DO - 10.1080/0094965031000104323

M3 - Article

SN - 0094-9655

VL - 74

JO - Journal of Statistical Computation and Simulation

JF - Journal of Statistical Computation and Simulation

ER -

Estimating the Variance of a Normal Population by Utilizing the Information in a Sample from a Second Related Normal

Abstract

Keywords

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