Open AccessEmploying particle swarm optimization algorithm for shrinkage parameter estimation in generalized Liu estimator
Author(s) -
Qamar Abdulkareem Abdulazeez,
Zakariya Yahya Algamal
Publication year - 2020
Publication title -
international journal of advanced statistics and probability
Language(s) - English
Resource type - Journals
ISSN - 2307-9045
DOI - 10.14419/ijasp.v8i1.30565
Subject(s) - estimator , particle swarm optimization , shrinkage , mathematics , shrinkage estimator , mean squared error , generalization , algorithm , multicollinearity , mathematical optimization , ordinary least squares , matrix (chemical analysis) , minimum variance unbiased estimator , bias of an estimator , statistics , regression analysis , mathematical analysis , materials science , composite material
It is well-known that in the presence of multicollinearity, the Liu estimator is an alternative to the ordinary least square (OLS) estimator and the ridge estimator. Generalized Liu estimator (GLE) is a generalization of the Liu estimator. However, the efficiency of GLE depends on appropriately choosing the shrinkage parameter matrix which is involved in the GLE. In this paper, a particle swarm optimization method, which is a metaheuristic continuous algorithm, is proposed to estimate the shrinkage parameter matrix. The simulation study and real application results show the superior performance of the proposed method in terms of prediction error.