Work

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

Year: 2024

Type: article

Abstract: 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.

Source: Axioms

DOI: 10.3390/axioms13120882

Author: Nesrin Güler

Institution: Sakarya 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: $2,728

Funder: Sakarya Üniversitesi