An unbiased estimator with prior information

The ordinary least square (OLS) estimator suffers a breakdown in the presence of multicollinearity. The estimator is still unbiased but possesses a significant variance. In this study, we proposed an unbiased modified ridge-type estimator as an alternative to the OLS estimator and the biased estimat...

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Bibliographic Details
Published in:	Arab journal of basic and applied sciences Vol. 27; no. 1; pp. 45 - 55
Main Authors:	Lukman, Adewale F., Ayinde, Kayode, Aladeitan, Benedicta, Bamidele, Rasak
Format:	Journal Article
Language:	English
Published:	Taylor & Francis 01-01-2020 Taylor & Francis Group
Subjects:	efficient modified ridge-type simulation study Unbiased
Online Access:	Get full text
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Summary:	The ordinary least square (OLS) estimator suffers a breakdown in the presence of multicollinearity. The estimator is still unbiased but possesses a significant variance. In this study, we proposed an unbiased modified ridge-type estimator as an alternative to the OLS estimator and the biased estimators for handling multicollinearity in linear regression models. The properties of this new estimator were derived. The estimator is also unbiased with minimum variance. A real-life application to the higher heating value of poultry waste from proximate analysis and simulation study generally supported the findings.
ISSN:	2576-5299 2576-5299
DOI:	10.1080/25765299.2019.1706799