New Versions of Liu-type Estimator in Weighted and non-weighted Mixed Regression Model

This paper considers and proposes new estimators that depend on the sample and on prior information in the case that they either are equally or are not equally important in the model. The prior information is described as linear stochastic restrictions. We study the properties and the performances o...

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Main Author: Mustafa Ismaeel Naif Alheety
Format: Article
Language:English
Published: University of Baghdad, College of Science for Women 2020-03-01
Series:مجلة بغداد للعلوم
Subjects:
Online Access:http://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/5022
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author Mustafa Ismaeel Naif Alheety
author_facet Mustafa Ismaeel Naif Alheety
author_sort Mustafa Ismaeel Naif Alheety
collection DOAJ
description This paper considers and proposes new estimators that depend on the sample and on prior information in the case that they either are equally or are not equally important in the model. The prior information is described as linear stochastic restrictions. We study the properties and the performances of these estimators compared to other common estimators using the mean squared error as a criterion for the goodness of fit. A numerical example and a simulation study are proposed to explain the performance of the estimators.
format Article
id doaj-art-074f2f226dda4c3a8b4436e46ab1af5d
institution Kabale University
issn 2078-8665
2411-7986
language English
publishDate 2020-03-01
publisher University of Baghdad, College of Science for Women
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series مجلة بغداد للعلوم
spelling doaj-art-074f2f226dda4c3a8b4436e46ab1af5d2025-08-20T03:58:10ZengUniversity of Baghdad, College of Science for Womenمجلة بغداد للعلوم2078-86652411-79862020-03-01171(Suppl.)New Versions of Liu-type Estimator in Weighted and non-weighted Mixed Regression ModelMustafa Ismaeel Naif AlheetyThis paper considers and proposes new estimators that depend on the sample and on prior information in the case that they either are equally or are not equally important in the model. The prior information is described as linear stochastic restrictions. We study the properties and the performances of these estimators compared to other common estimators using the mean squared error as a criterion for the goodness of fit. A numerical example and a simulation study are proposed to explain the performance of the estimators.http://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/5022Liu estimator, Mean squared error matrix, Mixed estimator, Stochastic restricted Liu estimator, Weighted mixed Liu estimator. Mathematics Subject Classification Primary: 62J05; Secondary 62J07.
spellingShingle Mustafa Ismaeel Naif Alheety
New Versions of Liu-type Estimator in Weighted and non-weighted Mixed Regression Model
مجلة بغداد للعلوم
Liu estimator, Mean squared error matrix, Mixed estimator, Stochastic restricted Liu estimator, Weighted mixed Liu estimator. Mathematics Subject Classification Primary: 62J05; Secondary 62J07.
title New Versions of Liu-type Estimator in Weighted and non-weighted Mixed Regression Model
title_full New Versions of Liu-type Estimator in Weighted and non-weighted Mixed Regression Model
title_fullStr New Versions of Liu-type Estimator in Weighted and non-weighted Mixed Regression Model
title_full_unstemmed New Versions of Liu-type Estimator in Weighted and non-weighted Mixed Regression Model
title_short New Versions of Liu-type Estimator in Weighted and non-weighted Mixed Regression Model
title_sort new versions of liu type estimator in weighted and non weighted mixed regression model
topic Liu estimator, Mean squared error matrix, Mixed estimator, Stochastic restricted Liu estimator, Weighted mixed Liu estimator. Mathematics Subject Classification Primary: 62J05; Secondary 62J07.
url http://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/5022
work_keys_str_mv AT mustafaismaeelnaifalheety newversionsofliutypeestimatorinweightedandnonweightedmixedregressionmodel