Another proposal about the new two-parameter estimator for linear regression model with correlated regressors

In this article, we present a new general class of biased estimators which includes some popular estimators as special cases and discuss its properties for multiple linear regression models when regressors are correlated. This proposal is based on some modification in the existing new two-parameter...

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Bibliographic Details
Published inCommunications in statistics. Simulation and computation Vol. 51; no. 6; pp. 3054 - 3072
Main Authors Ahmad, Shakeel, Aslam, Muhammad
Format Journal Article
LanguageEnglish
Published Philadelphia Taylor & Francis 03.06.2022
Taylor & Francis Ltd
Subjects
Estimators
Liu estimator
Mean squared error matrix
Multicollinearity
New two-parameter estimator
Parameter estimation
Parameter modification
Regression models
Two-parameter estimator
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Summary:In this article, we present a new general class of biased estimators which includes some popular estimators as special cases and discuss its properties for multiple linear regression models when regressors are correlated. This proposal is based on some modification in the existing new two-parameter estimator. Performance of the proposed estimator is compared with many of the leading estimators, using the mean squared error matrix criterion, mitigating the adverse effects of multicollinearity. An extensive simulation study has been provided with a numerical example to illustrate the superiority of the proposed estimator.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0361-0918
1532-4141
DOI:10.1080/03610918.2019.1705975