An unbiased estimator with prior information

Adewale, F. Lukman and Kayode, Ayinde and Benedicta, Aladeitan and Rasak, Bamidele (2020) An unbiased estimator with prior information. Arab Journal of Basic and Applied Sciences, 27 (1). pp. 45-55. ISSN 2576-5299

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Abstract

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.

Item Type:	Article
Subjects:	G Geography. Anthropology. Recreation > GB Physical geography H Social Sciences > HM Sociology Q Science > QA Mathematics Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4050 Electronic information resources
Divisions:	Faculty of Law, Arts and Social Sciences > School of Humanities
Depositing User:	Dr. Bamidele Rasak
Date Deposited:	16 Jan 2020 08:04
Last Modified:	16 Jan 2020 08:04
URI:	https://eprints.lmu.edu.ng/id/eprint/2738

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