Operations Research: International Conference Series
Vol 3, No 1 (2022)

Comparing the Performance of Prediction Model of Ridge and Elastic Net in Correlated Dataset

Richy Marcelino Bastiaan (Unknown)
Deiby Tineke Salaki (Unknown)
Djoni Hatidja (Unknown)

Article Info

Publish Date
05 Mar 2022

Abstract

Multicollinearity refers to a condition where high correlation between independent variables in linear regression model occurs.  In this case, using ordinary least squares (OLS) leads to unstable model. Some penalized regression approaches such as ridge and elastic-net regression can be applied to overcome the problem. Penalized regression estimates model by adding a constrain on the size of parameter regression. In this study, simulation dataset is generated, comprised of 100 observation and 95 independent variables with high correlation. This empirical study shows that elastic-net method outperforms the ridge regression and OLS.  In correlated dataset, the OLS is failed to produce a prediction model based on mean squared error (MSE)

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Journal Info
Operations Research: International Conference Series
Website

Abbrev

Orics

Publisher

Indonesian Operations Research Association

Subject

Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Engineering Mathematics

Description

Operations Research: International Conference Series (ORICS) is published 4 times a year and is the flagship journal of the Indonesian Operational Research Association (IORA). It is the aim of ORICS to present papers which cover the theory, practice, history or methodology of OR. However, since OR ...