AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
PDF (268.4 KB)
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Research Article | Open Access

Statistical inference of a stochastically restricted linear mixed model

Nesrin Güler1( )Melek Eriş Büyükkaya2
Department of Econometrics, Sakarya University, Sakarya 54187, Turkey
Department of Statistics and Computer Sciences, Karadeniz Technical University, Trabzon 61080, Turkey
Show Author Information

Abstract

This article compares a predictor with the best linear unbiased predictor (BLUP) for a unified form of all unknown parameters under a stochastically restricted linear mixed model (SRLMM) in terms of the mean squared error matrix (MSEM) criterion. The methodology of block matrix inertias and ranks is employed to compare the MSEMs of these predictors. The comparison results are also demonstrated for a linear mixed model with and without an exact restriction, as well as special cases of the unified form of all unknown parameters in the SRLMM.

CLC number: 15A03, 62H12, 62J05

References

【1】
【1】
 
 
AIMS Mathematics
Pages 24401-24417

{{item.num}}

Comments on this article

Go to comment

< Back to all reports

Review Status: {{reviewData.commendedNum}} Commended , {{reviewData.revisionRequiredNum}} Revision Required , {{reviewData.notCommendedNum}} Not Commended Under Peer Review

Review Comment

Close
Close
Cite this article:
Güler N, Büyükkaya ME. Statistical inference of a stochastically restricted linear mixed model. AIMS Mathematics, 2023, 8(10): 24401-24417. https://doi.org/10.3934/math.20231244

116

Views

2

Downloads

7

Crossref

6

Web of Science

6

Scopus

Received: 02 May 2023
Revised: 01 August 2023
Accepted: 06 August 2023
Published: 15 October 2023
©2023 the Author(s), licensee AIMS Press.

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0)