Work

Statistical inference of the mixed linear model with incorrect stochastic linear restrictions

Year: 2025

Type: article

Abstract: We considered the general mixed linear model $ \mathscr{N} $ subject to two competing stochastic linear restrictions, $ \mathscr{M}_{0} $ and $ \mathscr{M} $, where the restrictions $ \mathscr{M} $ are the correct information whereas restrictions $ \mathscr{M}_{0} $ may be incorrect. Statistical inference conclusions of using the above two competing restrictions are not necessarily the same, so it is prominent to discuss the relationships between incorrect restrictions $ \mathscr{M}_{0} $ and the corresponding correct restrictions $ \mathscr{M} $ in the context of model $ \mathscr{N} $. In this article, we first present some properties on the best linear unbiased predictors (BLUPs) under model $ \mathscr{N} $ with restrictions $ \mathscr{M} $. We then provide necessary and sufficient conditions under which the BLUPs under $ \mathscr{N} $ with the incorrect restrictions $ \mathscr{M}_{0} $ continue to be BLUPs associated with correct restrictions.

Source: AIMS Mathematics

DOI: 10.3934/math.2025516

Author: Xingwei Ren

Institutions: Anhui University of Science and Technology, Anhui Science and Technology University

Language: English

Topic: Advanced Statistical Methods and Models

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $1,200

Funder: Anhui University of Science and Technology