Liu Estimates and Influence Analysis in Regression Models with Stochastic Linear Restrictions and AR (1) Errors

In the linear regression models with AR (1) error structure when collinearity exists, stochastic linear restrictions or modifications of biased estimators (including Liu estimators) can be used to reduce the estimated variance of the regression coefficients estimates. In this paper, the combination...

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Main Authors: Hoda Mohammadi, Abdolrahman Rasekh
Format: Article
Language:English
Published: University of Tehran 2019-07-01
Series:Journal of Sciences, Islamic Republic of Iran
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Online Access:https://jsciences.ut.ac.ir/article_71760_85030dff5e3f5a47965027bfddd9f1c1.pdf
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author Hoda Mohammadi
Abdolrahman Rasekh
author_facet Hoda Mohammadi
Abdolrahman Rasekh
author_sort Hoda Mohammadi
collection DOAJ
description In the linear regression models with AR (1) error structure when collinearity exists, stochastic linear restrictions or modifications of biased estimators (including Liu estimators) can be used to reduce the estimated variance of the regression coefficients estimates. In this paper, the combination of the biased Liu estimator and stochastic linear restrictions estimator is considered to overcome the effect of collinearity on the estimated coefficients. In addition, the deletion formulas for the detection of influential observations are presented for the proposed estimator. Finally, a simulation study and numerical example have been conducted to show the superiority of the proposed procedures.
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publishDate 2019-07-01
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spelling doaj-art-7f8c0af30b8d4fda991f45f7718892732025-08-20T02:25:54ZengUniversity of TehranJournal of Sciences, Islamic Republic of Iran1016-11042345-69142019-07-0130327128510.22059/jsciences.2019.275988.100737571760Liu Estimates and Influence Analysis in Regression Models with Stochastic Linear Restrictions and AR (1) ErrorsHoda Mohammadi0Abdolrahman Rasekh1Department of Statistics, Faculty of Mathematics and Computer Sciences, Shahid Chamran University of Ahvaz, Ahvaz, Islamic Republic of IranDepartment of Statistics, Shahid Chamran University of AhvazIn the linear regression models with AR (1) error structure when collinearity exists, stochastic linear restrictions or modifications of biased estimators (including Liu estimators) can be used to reduce the estimated variance of the regression coefficients estimates. In this paper, the combination of the biased Liu estimator and stochastic linear restrictions estimator is considered to overcome the effect of collinearity on the estimated coefficients. In addition, the deletion formulas for the detection of influential observations are presented for the proposed estimator. Finally, a simulation study and numerical example have been conducted to show the superiority of the proposed procedures.https://jsciences.ut.ac.ir/article_71760_85030dff5e3f5a47965027bfddd9f1c1.pdfliu estimatorlinear stochastic restrictionscollinearityautocorrelated errorinfluence analysis
spellingShingle Hoda Mohammadi
Abdolrahman Rasekh
Liu Estimates and Influence Analysis in Regression Models with Stochastic Linear Restrictions and AR (1) Errors
Journal of Sciences, Islamic Republic of Iran
liu estimator
linear stochastic restrictions
collinearity
autocorrelated error
influence analysis
title Liu Estimates and Influence Analysis in Regression Models with Stochastic Linear Restrictions and AR (1) Errors
title_full Liu Estimates and Influence Analysis in Regression Models with Stochastic Linear Restrictions and AR (1) Errors
title_fullStr Liu Estimates and Influence Analysis in Regression Models with Stochastic Linear Restrictions and AR (1) Errors
title_full_unstemmed Liu Estimates and Influence Analysis in Regression Models with Stochastic Linear Restrictions and AR (1) Errors
title_short Liu Estimates and Influence Analysis in Regression Models with Stochastic Linear Restrictions and AR (1) Errors
title_sort liu estimates and influence analysis in regression models with stochastic linear restrictions and ar 1 errors
topic liu estimator
linear stochastic restrictions
collinearity
autocorrelated error
influence analysis
url https://jsciences.ut.ac.ir/article_71760_85030dff5e3f5a47965027bfddd9f1c1.pdf
work_keys_str_mv AT hodamohammadi liuestimatesandinfluenceanalysisinregressionmodelswithstochasticlinearrestrictionsandar1errors
AT abdolrahmanrasekh liuestimatesandinfluenceanalysisinregressionmodelswithstochasticlinearrestrictionsandar1errors