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

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

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...

Full description

Saved in:
Bibliographic Details
Published in:Scientifica (Cairo) Vol. 2020; no. 2020; pp. 1 - 16
Main Authors: Kibria, B. M. Golam, Lukman, Adewale F.
Format: Journal Article
Language:English
Published: Cairo, Egypt Hindawi Publishing Corporation 2020
Hindawi
Hindawi Limited
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
Academic Editor: Osman Kucuk
ISSN:2090-908X
2090-908X
DOI:10.1155/2020/9758378