Estimating a Bounded Normal Mean Relative to Squared Error Loss Function

Let be a random sample from a normal distribution with unknown mean and known variance The usual estimator of the mean, i.e., sample mean is the maximum likelihood estimator which under squared error loss function is minimax and admissible estimator. In many practical situations, is known...

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Main Author: A. Karimnezhad
Format: Article
Language:English
Published: University of Tehran 2011-09-01
Series:Journal of Sciences, Islamic Republic of Iran
Subjects:
Online Access:https://jsciences.ut.ac.ir/article_23589_c00c9b7bdf289e4d84aa381dfa227d26.pdf
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author A. Karimnezhad
author_facet A. Karimnezhad
author_sort A. Karimnezhad
collection DOAJ
description Let be a random sample from a normal distribution with unknown mean and known variance The usual estimator of the mean, i.e., sample mean is the maximum likelihood estimator which under squared error loss function is minimax and admissible estimator. In many practical situations, is known in advance to lie in an interval, say for some In this case, the maximum likelihood estimator changes and dominates but it is no longer admissible. Minimax and some other estimators for this problem have been studied by some researchers. In this paper, a new estimator is proposed and the risk function of it is compared with some other competitors. According to our findings, the use of and the maximum likelihood estimator is not recommended when some information are accessible about the finite bounds on in advance. Based on the values taken by in , the appropriate estimator is suggested.
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institution Kabale University
issn 1016-1104
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language English
publishDate 2011-09-01
publisher University of Tehran
record_format Article
series Journal of Sciences, Islamic Republic of Iran
spelling doaj-art-cec4b2a58b9642bf9be60c78718cc26c2025-08-20T03:53:51ZengUniversity of TehranJournal of Sciences, Islamic Republic of Iran1016-11042345-69142011-09-0122326727623589Estimating a Bounded Normal Mean Relative to Squared Error Loss FunctionA. Karimnezhad0Allameh Tabataba’i UniversityLet be a random sample from a normal distribution with unknown mean and known variance The usual estimator of the mean, i.e., sample mean is the maximum likelihood estimator which under squared error loss function is minimax and admissible estimator. In many practical situations, is known in advance to lie in an interval, say for some In this case, the maximum likelihood estimator changes and dominates but it is no longer admissible. Minimax and some other estimators for this problem have been studied by some researchers. In this paper, a new estimator is proposed and the risk function of it is compared with some other competitors. According to our findings, the use of and the maximum likelihood estimator is not recommended when some information are accessible about the finite bounds on in advance. Based on the values taken by in , the appropriate estimator is suggested.https://jsciences.ut.ac.ir/article_23589_c00c9b7bdf289e4d84aa381dfa227d26.pdfadmissibilitysquared error lossbounded normal meanmaximum likelihood estimatorrao-blackwellization
spellingShingle A. Karimnezhad
Estimating a Bounded Normal Mean Relative to Squared Error Loss Function
Journal of Sciences, Islamic Republic of Iran
admissibility
squared error loss
bounded normal mean
maximum likelihood estimator
rao-blackwellization
title Estimating a Bounded Normal Mean Relative to Squared Error Loss Function
title_full Estimating a Bounded Normal Mean Relative to Squared Error Loss Function
title_fullStr Estimating a Bounded Normal Mean Relative to Squared Error Loss Function
title_full_unstemmed Estimating a Bounded Normal Mean Relative to Squared Error Loss Function
title_short Estimating a Bounded Normal Mean Relative to Squared Error Loss Function
title_sort estimating a bounded normal mean relative to squared error loss function
topic admissibility
squared error loss
bounded normal mean
maximum likelihood estimator
rao-blackwellization
url https://jsciences.ut.ac.ir/article_23589_c00c9b7bdf289e4d84aa381dfa227d26.pdf
work_keys_str_mv AT akarimnezhad estimatingaboundednormalmeanrelativetosquarederrorlossfunction