A New Ridge-Type Estimator for the Linear Regression Model: Simulations and Applications-Reference-Cited by-同舟云学术

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

Published:2020-04-28 Issue: Volume:2020 Page:1-16
ISSN:2090-908X
Container-title:Scientifica
language:en
Short-container-title:Scientifica

Author:

Kibria B. M. Golam¹^ORCID,Lukman Adewale F.²³^ORCID

Affiliation:

1. Department of Mathematics and Statistics, Florida International University, Miami, FL, USA

2. Department of Physical Sciences, Landmark University, Omu-Aran, Nigeria

3. Institut Henri Poincare Centre Emile Borel, Paris, France

Abstract

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.

Publisher

Hindawi Limited

Subject

General Agricultural and Biological Sciences,General Environmental Science

Link

http://downloads.hindawi.com/journals/scientifica/2020/9758378.pdf

Reference39 articles.

1. Principal Components Regression in Exploratory Statistical Research

2. Ridge Regression: Biased Estimation for Nonorthogonal Problems

3. On Biased Estimation in Linear Models

4. Good ridge estimators based on prior information

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