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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| Format: | Article |
| Language: | English |
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University of Baghdad, College of Science for Women
2020-03-01
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| Series: | مجلة بغداد للعلوم |
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| Online Access: | http://bsj.uobaghdad.edu.iq/index.php/BSJ/article/view/5022 |
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| _version_ | 1849247637700608000 |
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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 |
| record_format | Article |
| 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 |