Comparison of predictors under constrained general linear model and its future observations

Büyükkaya, Melek Eriş

doi:10.1080/03610926.2024.2314618

Communications in Statistics - Theory and Methods

2024

DOI: 10.1080/03610926.2024.2314618

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Comparison of predictors under constrained general linear model and its future observations

Melek Eriş Büyükkaya

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2024

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Cited by 2 publications

References 39 publications

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On a Biased Prediction Based on Optimal Mean Square Error Criterion

Jiang,

Tian

2024

Bull. Malays. Math. Sci. Soc.

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On a Biased Prediction Based on Optimal Mean Square Error Criterion

Jiang,

Tian

2024

Bull. Malays. Math. Sci. Soc.

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Analysis of Optimal Prediction Under Stochastically Restricted Linear Model and Its Subsample Models

Güler

2024

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This paper provides a study on optimal prediction problems in a linear model and its subsample models with linear stochastic restrictions, using matrix theory for precise analytical solutions. It focuses on deriving analytical expressions using block matrix inertia and rank methods to determine which of the best linear unbiased predictors (BLUPs) of a general vector of unknown parameters is superior to others under a stochastically restricted linear model and its subsample models. Additionally, this study examines the comparative results of the best linear unbiased estimators of unknown parameters. The comparisons in the study are based on the mean squared error matrix (MSEM) criterion. Finally, a numerical example is given to illustrate the theoretical results.

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Comparison of predictors under constrained general linear model and its future observations

Cited by 2 publications

References 39 publications

On a Biased Prediction Based on Optimal Mean Square Error Criterion

On a Biased Prediction Based on Optimal Mean Square Error Criterion

Analysis of Optimal Prediction Under Stochastically Restricted Linear Model and Its Subsample Models

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