Alternative ridge parameters in linear model-Reference-Cited by-同舟云学术

Alternative ridge parameters in linear model

Published:2022-03-28 Issue: Volume: Page:
ISSN:2667-8993
Container-title:Nicel Bilimler Dergisi
language:tr
Short-container-title:NBD

Author:

AYİNDE Kayode¹,ADEWUYİ Emmanuel²,FOLARANMİ Lukman Adewale³

Affiliation:

1. Federal University of Technology, Akure

2. Ladoke Akintola university of technology, Ogbomoso

3. University of Medical Sciences

Abstract

The ridge regression estimator produces efficient estimates than the Ordinary Least Square Estimator in a linear regression model that has multicollinearity problem. However, the efficiency of the ridge estimator depends on the choice of the ridge parameter, k. The ridge parameters are classified into different forms, various types, and diverse kinds. These classifications resulted in proposing some other techniques of Ridge parameter estimation. Investigation of the existing and proposed ridge parameters was done by conducting Monte-Carlo experiments. Results from the simulation study and real-life data application show that some newly proposed ridge parameters are among those that provide efficient estimates.

Publisher

Nicel Bilimler Dergisi based at ESOGU Istatistik Danismanlik Uygulama ve Arastirma Merkezi

Subject

Psychiatry and Mental health,Neuropsychology and Physiological Psychology

Reference19 articles.

1. Ajiboye S. A., Adewuyi, E., Ayinde, K. and Lukman, A. F. (2016). A Comparative Study of Some Robust Ridge and Liu Estimators. Science World Journal, 11(4):16-20. Alkhamisi, M. and Shukur, G. (2008). Developing ridge parameters for SUR model. Communications in Statistics - Theory and Methods, 37(4): 544-564. Alkhamisi, M., Khalaf, G. and Shukur, G. (2006). Some modifications for choosing ridge parameters. Communications in Statistics - Theory and Methods, 35(11): 2005-2020.

2. Batach, F., Gore, S. and Verma, M. (2008). Effect of Jackknifing on Various Ridge Type Estimators. Model Assisted Statistics and Applications, 3, 121 – 130.

3. Daniel, C. and Wood, F. S. (1980). Fitting Equations to Data: Computer Analysis of Multifactor Data. Second Edition. Dempster, A. P., Schatzoff, M., and Wermuth, N. (1977). A Simulation Study of Alternatives to Ordinary Least Squares. Journal of the American Statistical Association, 72(357): 77-91. doi: 10.1080/01621459.1977.10479910

4. Dorugade, A. V. and Kashid, D. N. (2010). Alternative method for choosing ridge parameter for regression. International Journal of Applied Mathematical Sciences, 4(9): 447-456.

5. Faraway J. (2002). Practical Regression and Anova using R; 109 – 113

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