Open AccessModified Almost Unbiased Two-Parameter Estimator in linear regression model
Author(s) -
Adewale F. Lukman,
Emmanuel T. Adewuyi,
N. K. Oladejo,
Ayinde Samuel Olukayode
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/640/1/012119
Subject(s) - minimum variance unbiased estimator , stein's unbiased risk estimate , ordinary least squares , bias of an estimator , estimator , mathematics , mean squared error , efficient estimator , statistics , consistent estimator , best linear unbiased prediction , trimmed estimator , computer science , artificial intelligence , selection (genetic algorithm)
Wu and Yang [1] proposed an almost unbiased two-parameter estimator as an alternative to the ordinary least squares estimator (OLSE) in a multicollinear regression model. We introduce modified almost unbiased two-parameter estimator by combining two ideas. The mean squared error matrix of the proposed estimator was derived and the performance compared with some existing estimators. The theoretical results were accompanied by a numerical example and a simulation study.