Adjusted ridge estimator and comparison with Kibria’s method in linear regression

dc.contributor.author	Dorugade, A.V.
dc.date.accessioned	2018-07-30T05:49:43Z
dc.date.available	2018-07-30T05:49:43Z
dc.date.issued	2016
dc.identifier.issn	1815-3852
dc.identifier.uri	https://journal.uob.edu.bh:443/handle/123456789/1147
dc.description.abstract	This paper proposes an adjusted ridge regression estimator for b for the linear regression model. The merit of the proposed estimator is that it does not require estimating the ridge parameter k unlike other existing estimators. We compared our estimator with an ordinary least squares (LS) estimator and with some well known estimators proposed by Hoerl and Kennard (1970), ordinary ridge regression (RR) estimator and generalized ridge regression (GR) and some estimators proposed by Kibria (2003) among others. A simulation study has been conducted and compared for the performance of the estimators in the sense of smaller mean square error (MSE). It appears that the proposed estimator is promising and can be recommended to the practitioners.	en_US
dc.language.iso	en	en_US
dc.publisher	University of Bahrain	en_US
dc.rights	Attribution-NonCommercial-ShareAlike 4.0 International	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-sa/4.0/	*
dc.subject	Ridge regression
dc.subject	Ridge estimator
dc.subject	Mean square error
dc.subject	Simulation
dc.title	Adjusted ridge estimator and comparison with Kibria’s method in linear regression	en_US
dc.type	Article	en_US
dc.source.title	Arab Journal of Basic and Applied Sciences
dc.abbreviatedsourcetitle	AJBAS