Detection andDealing ofMulticollinearity Using aNew RidgeEstimator

Title

Detection andDealing ofMulticollinearity Using aNew RidgeEstimator

Creator

Augustine, Praveen; John, Nimitha

Description

The Ordinary Least Square (OLS) method is a widely used technique for estimating regression coefficients that describe the relationship between the independent variables and the dependent variable. The regression estimates obtained using this approach give a poor result in the presence of multicollinearity. Ridge regression is employed to address this problem. A ridge estimator, also called a biasing constant, plays a notable role in the parameter estimation of ridge regression. In this paper, we introduced a new ridge estimator and studied its properties. A comparative study of the proposed estimator with some of the existing ridge estimators under extreme multicollinearity has been illustrated by using a simulation technique. The estimator developed in this paper seems to perform better than all the other estimators because of the smaller ratio of average mean square error. The performance of the proposed estimator is analysed and verified using real-life data. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.

Source

Lecture Notes in Networks and Systems;Volume;1501 LNNS;pp.365-378

Date

01-01-2026

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

Generalised ridge regression; Multicollinearity; Ridge estimator; Ridge regression

Coverage

Augustine P., Department of Data Science and Statistics, CHRIST (Deemed to Be University), Bengaluru, India; John N., Department of Data Science and Statistics, CHRIST (Deemed to Be University), Bengaluru, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISSN: 23673370; ISBN: 978-981968349-9;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-96-8350-5_26

https://www.scopus.com/pages/publications/105028352404?origin=resultslist