Ridge estimation in linear mixed measurement error models using generalized maximum entropy. Issue 5 (3rd September 2022)

• Record Type: Journal Article
• Authors: Janamiri, Fariba; Rasekh, Abdolrahman; Chaji, Alireza; Babadi, Babak
• Is Part Of: Statistics. Volume 56:Issue 5(2022)
• Page Start: 1095
• Page End: 1112
• Publication Date: 2022-09-03
• Subjects: Linear mixed measurement error models -- collinearity -- generalized maximum entropy -- ridge regression -- ridge-GME estimation; Mathematical statistics -- Periodicals; 519.505
• DOI: 10.1080/02331888.2022.2126474
• Languages: English
• ISSNs: 0233-1888

Abstract

Abstract : In this article, we concentrate on the generalized maximum entropy (GME) estimators and their asymptotic properties in linear mixed models (LMMs) with measurement error in the fixed effects (MEFE) variables. Moreover, we obtain the Ridge-GME estimator in these models as a remedy to collinearity. Finally, a simulation study and an example of real data are given to characterize the superiority of the Ridge-GME estimator over the corrected score estimator (CSE) and ridge estimator (RE), using the mean squared error matrix (MSEM).