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2014•Sri Lankan Journal of Applied StatisticsOpen access

On Comparison of Some Ridge Parameters in Ridge Regression

A. V. Dorugade

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Abstract

In this article, a new approach to obtain the ridge parameter introduces for the multiple linear regression model suffers from the problem of multicollinearity. Furthermore, we compare the proposed ridge parameter with the other well-known ridge-parameters through ridge estimators evaluated elsewhere in terms of mean squares error (MSE) criterion. Finally, a numerical example and simulation study has been conducted to illustrate the optimality of the proposed ridge parameter. DOI: http://dx.doi.org/10.4038/sljastats.v15i1.6792

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What this paper is about

In this article, a new approach to obtain the ridge parameter introduces for the multiple linear regression model suffers from the problem of multicollinearity. Furthermore, we compare the proposed ridge parameter with the other well-known ridge-parameters through ridge estimators evaluated elsewhere in terms of mean squares error (MSE) criterion. Finally, a numerical example and simulation study has been conducted to illustrate the optimality of the proposed ridge parameter. DOI: http://dx.doi.org/10.4038/sljastats.v15i1.6792

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Available abstract

In this article, a new approach to obtain the ridge parameter introduces for the multiple linear regression model suffers from the problem of multicollinearity. Furthermore, we compare the proposed ridge parameter with the other well-known ridge-parameters through ridge estimators evaluated elsewhere in terms of mean squares error (MSE) criterion. Finally, a numerical example and simulation study has been conducted to illustrate the optimality of the proposed ridge parameter. DOI: http://dx.doi.org/10.4038/sljastats.v15i1.6792

Key concepts: Multicollinearity, Ridge, Estimator, Regression, Linear regression, Statistics, Mean squared error, Mathematics

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