Optimal generalized logistic estimator

Please use this identifier to cite or link to this item: http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/4271

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DC Field	Value	Language
dc.contributor.author	Varathan, N.
dc.contributor.author	Wijekoon, P.
dc.date.accessioned	2021-11-29T04:30:33Z
dc.date.accessioned	2022-06-28T06:46:05Z	-
dc.date.available	2021-11-29T04:30:33Z
dc.date.available	2022-06-28T06:46:05Z	-
dc.date.issued	2018
dc.identifier.uri	http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/4271	-
dc.description.abstract	In this paper, we propose a new efficient estimator namely Optimal Generalized Logistic Estimator (OGLE) for estimating the parameter in a logistic regression model when there exists multicollinearity among explanatory variables. Asymptotic properties of the proposed estimator are also derived. The performance of the proposed estimator over the other existing estimators in respect of Scalar Mean Square Error crite rion is examined by conducting a Monte Carlo simulation.	en_US
dc.language.iso	en	en_US
dc.publisher	University of Jaffna	en_US
dc.subject	logistic regression;	en_US
dc.subject	biased	en_US
dc.subject	multicollinearity	en_US
dc.subject	estimator	en_US
dc.subject	Optimal generalized logistic estimator	en_US
dc.subject	Scalar mean square error	en_US
dc.title	Optimal generalized logistic estimator	en_US
dc.type	Article	en_US
Appears in Collections:	Mathematics and Statistics

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