Statistical inference of a stochastically restricted linear mixed model

Nesrin Güler; Melek Eriş Büyükkaya; Nesrin Güler; Melek Eriş Büyükkaya

doi:10.3934/math.20231244

AIMS Mathematics

2023, Volume 8, Issue 10: 24401-24417. doi: 10.3934/math.20231244

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Research article

Statistical inference of a stochastically restricted linear mixed model

Nesrin Güler ^{1
,
,},
Melek Eriş Büyükkaya ²

1.
Department of Econometrics, Sakarya University, Sakarya 54187, Turkey
2.
Department of Statistics and Computer Sciences, Karadeniz Technical University, Trabzon 61080, Turkey

Received: 02 May 2023 Revised: 01 August 2023 Accepted: 06 August 2023 Published: 16 August 2023
MSC : 15A03, 62H12, 62J05

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.
- BLUP,
- comparison,
- linear mixed model,
- linear stochastic restriction,
- MSEM
Citation: Nesrin Güler, Melek Eriş Büyükkaya. Statistical inference of a stochastically restricted linear mixed model[J]. AIMS Mathematics, 2023, 8(10): 24401-24417. doi: 10.3934/math.20231244

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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.

References

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