A jackknifed ridge estimator in probit regression model. Issue 4 (3rd July 2020)

• Record Type: Journal Article
• Authors: Asar, Yasin; Kılınç, Kadriye
• Is Part Of: Statistics. Volume 54:Issue 4(2020)
• Page Start: 667
• Page End: 685
• Publication Date: 2020-07-03
• Subjects: Multicollinearity -- ridge estimator -- jackknifed ridge estimator -- probit model -- mean squared error; 62J05 -- 62J07; Mathematical statistics -- Periodicals; 519.505
• DOI: 10.1080/02331888.2020.1775597
• Languages: English
• ISSNs: 0233-1888

Abstract

Abstract : In this study, the effects of multicollinearity on the maximum likelihood estimator are analyzed in the probit regression model. It is known that the near-linear dependencies in the design matrix affect the maximum likelihood estimation negatively, namely, the standard errors become so large so that the estimations are said to be inconsistent. Therefore, a new jackknifed ridge estimator is introduced as an alternative to the maximum likelihood technique and the well-known ridge estimator. The mean squared error properties of the listed estimators are investigated theoretically. In order to evaluate the performance of the estimators, a Monte Carlo simulation study is designed, and simulated mean squared error and squared bias are used as performance criteria. Finally, the benefits of the new estimator are illustrated via a real data application.