Development of robust Özkale–Kaçiranlar and Yang–Chang estimators for regression models in the presence of multicollinearity and outliers. (17th December 2021)

• Record Type: Journal Article
• Authors: Awwad, Fuad A.; Dawoud, Issam; Abonazel, Mohamed R.
• Is Part Of: Concurrency and computation. Volume 34:Number 6(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-12-17
• Subjects: biased estimation -- biasing parameter -- manufacturing production -- multicollinearity -- outliers -- robust regression; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.6779
• Languages: English
• ISSNs: 1532-0626

Abstract

Abstract: The ordinary least‐squares estimator is commonly used to estimate the parameters of a linear regression model but gives unreliable and unfavorable results when two problems occur together: multicollinearity and outliers. This article proposes two different robust estimators of the regression parameters to cope with these problems together. The proposed estimators are a robust version of the Özkale–Kaçiranlar and Yang–Chang estimators. Theoretical calculations, numerical simulations, and real‐life data on manufacturing production are presented to demonstrate the superiority of the proposed robust estimators to existing estimators at dealing with multicollinearity and outliers at the same time.