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Copyright (c) 2016 Pakistan Journal of Statistics and Operation Research

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This work is licensed under a Creative Commons Attribution 4.0 International License.

Title

A Comparative Study on the Performance of New Ridge Estimators

Keywords

Multiple Linear Regression, Multicollinearity, Ridge Parameter and MSE.

Description

Least square estimators in multiple linear regressions under multicollinearity become unstable as they produce large variance for the estimated regression coefficients. Hoerl and Kennard 1970, developed ridge estimators for cases of high degree of collinearity. In ridge estimation, the estimation of ridge parameter (k ) is vital. In this article new methods for estimating ridge parameter are introduced. The performance of the proposed estimators is investigated through mean square errors (MSE). Monte-Carlo simulation technique indicated that the proposed estimators perform better than ordinary least squares (OLS) estimators as well as few other ridge estimators.


Date

2016-06-03

Identifier


Source

Pakistan Journal of Statistics and Operation Research; Vol. 12 No. 2, 2016



Print ISSN: 1816-2711 | Electronic ISSN: 2220-5810