Open AccessPerformance of stochastic restricted and unrestricted two-parameter estimators in linear mixed models
Author(s) -
Nahid Ganjealivand,
Fatemeh Ghapani,
Ali Zaherzadeh,
Farshin Hormozinejad
Publication year - 2021
Publication title -
revista internacional de métodos numéricos para cálculo y diseño en ingeniería
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.213
H-Index - 9
eISSN - 1886-158X
pISSN - 0213-1315
DOI - 10.23967/j.rimni.2021.06.001
Subject(s) - estimator , minimum variance unbiased estimator , mathematics , mean squared error , estimation theory , linear model , best linear unbiased prediction , bias of an estimator , statistics , mathematical optimization , computer science , artificial intelligence , selection (genetic algorithm)
In this article, two parameter estimation using penalized likelihood method in the linear mixed model is proposed. In addition, by considering the stochastic linear restriction for the vector of fixed effects parameters we are introduced the stochastic restricted two parameter estimation. Methods are proposed for estimating variance parameters when unknown. Also, the superiority conditions of the two parameter estimator over the best linear unbiased estimator, and the stochastic restricted two parameter estimator over the stochastic restricted best linear unbiased estimator are obtained under the mean square error matrix sense. Methods are proposed for estimating of the biasing parameters. Finally, a simulation study and a numerical example are given to evaluate the proposed estimators