A New Ridge-Type Estimator for the Linear Regression Model: Simulations and Applications
Joint Authors
Kibria, B. M. Golam
Lukman, Adewale F.
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-16, 16 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-04-28
Country of Publication
Egypt
No. of Pages
16
Main Subjects
Abstract EN
The ridge regression-type (Hoerl and Kennard, 1970) and Liu-type (Liu, 1993) estimators are consistently attractive shrinkage methods to reduce the effects of multicollinearity for both linear and nonlinear regression models.
This paper proposes a new estimator to solve the multicollinearity problem for the linear regression model.
Theory and simulation results show that, under some conditions, it performs better than both Liu and ridge regression estimators in the smaller MSE sense.
Two real-life (chemical and economic) data are analyzed to illustrate the findings of the paper.
American Psychological Association (APA)
Kibria, B. M. Golam& Lukman, Adewale F.. 2020. A New Ridge-Type Estimator for the Linear Regression Model: Simulations and Applications. Scientifica،Vol. 2020, no. 2020, pp.1-16.
https://search.emarefa.net/detail/BIM-1208284
Modern Language Association (MLA)
Kibria, B. M. Golam& Lukman, Adewale F.. A New Ridge-Type Estimator for the Linear Regression Model: Simulations and Applications. Scientifica No. 2020 (2020), pp.1-16.
https://search.emarefa.net/detail/BIM-1208284
American Medical Association (AMA)
Kibria, B. M. Golam& Lukman, Adewale F.. A New Ridge-Type Estimator for the Linear Regression Model: Simulations and Applications. Scientifica. 2020. Vol. 2020, no. 2020, pp.1-16.
https://search.emarefa.net/detail/BIM-1208284
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1208284