Robust-stein estimator for overcoming outliers and multicollinearity

Autor:	Adewale F. Lukman, Rasha A. Farghali, B. M. Golam Kibria, Okunlola A. Oluyemi
Jazyk:	angličtina
Rok vydání:	2023
Předmět:	Medicine Science
Zdroj:	Scientific Reports, Vol 13, Iss 1, Pp 1-19 (2023)
Druh dokumentu:	article
ISSN:	2045-2322
DOI:	10.1038/s41598-023-36053-z
Popis:	Abstract Linear regression models with correlated regressors can negatively impact the performance of ordinary least squares estimators. The Stein and ridge estimators have been proposed as alternative techniques to improve estimation accuracy. However, both methods are non-robust to outliers. In previous studies, the M-estimator has been used in combination with the ridge estimator to address both correlated regressors and outliers. In this paper, we introduce the robust Stein estimator to address both issues simultaneously. Our simulation and application results demonstrate that the proposed technique performs favorably compared to existing methods.
Databáze:	Directory of Open Access Journals
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