A New Ridge-Type Estimator for the Linear Regression Model: Simulations and Applications

被引：96

作者：

Kibria, B. M. Golam ^{[1
]}

Lukman, Adewale F. ^{[2
,3
]}

机构：

[1] Florida Int Univ, Dept Math & Stat, Miami, FL 33199 USA

[2] Landmark Univ, Dept Phys Sci, Omu Aran, Nigeria

[3] Ctr Emile Borel, Inst Henri Poincare, Paris, France

来源：

SCIENTIFICA | 2020年 / 2020卷

关键词：

LIU-TYPE ESTIMATOR; BIASED-ESTIMATION; MONTE-CARLO;

D O I：

10.1155/2020/9758378

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

The ridge regression-type (Hoerl and Kennard, 1970) and Liu-type (Liu, 1993) estimators are consistently attractive shrinkage methods to reduce the effects of multicollinearity for both linear and nonlinear regression models. This paper proposes a new estimator to solve the multicollinearity problem for the linear regression model. Theory and simulation results show that, under some conditions, it performs better than both Liu and ridge regression estimators in the smaller MSE sense. Two real-life (chemical and economic) data are analyzed to illustrate the findings of the paper.

引用

页数：16

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