Estimation of parameters in linear mixed measurement error models with stochastic linear restrictions. Issue 23 (1st December 2020)

• Record Type: Journal Article
• Authors: Yavarizadeh, Bahareh; Rasekh, Abdolrahman; Babadi, Babak
• Is Part Of: Communications in statistics. Volume 49:Issue 23(2020)
• Page Start: 5853
• Page End: 5865
• Publication Date: 2020-12-01
• Subjects: Linear mixed measurement error models -- corrected score function -- collinearity -- prior information; 62J02 -- 62F12; Mathematical statistics -- Periodicals; Mathematics; Statistics; 519.2
• DOI: 10.1080/03610926.2019.1622730
• Languages: English
• ISSNs: 0361-0926

Abstract

Abstract: In this paper, we concentrate on the estimation of parameters in the linear mixed models (LMMs) with stochastic linear restrictions on the fixed and random effects, when the fixed effects are measured with error. In addition, the asymptotic properties of the estimators are derived and their performances are compared with the estimators for no restrictions cases, in the sense of mean square error matrix (MSEM). Finally, a simulation study and numerical example have been conducted to show the superiority of the restricted estimator over the unrestricted estimator.