An unbiased estimator with prior information
| Autor: | Adewale F. Lukman, Kayode Ayinde, Benedicta Aladeitan, Rasak Bamidele |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: | |
| Zdroj: | Arab Journal of Basic and Applied Sciences, Vol 27, Iss 1, Pp 45-55 (2020) |
| Druh dokumentu: | article |
| ISSN: | 2576-5299 25765299 |
| DOI: | 10.1080/25765299.2019.1706799 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
| Externí odkaz: |
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