Please use this identifier to cite or link to this item: http://repo.lib.jfn.ac.lk/ujrr/handle/123456789/4300
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dc.contributor.authorVarathan, N.
dc.contributor.authorWijekoon, P.
dc.date.accessioned2021-11-30T05:01:19Z
dc.date.accessioned2022-06-28T06:46:06Z-
dc.date.available2021-11-30T05:01:19Z
dc.date.available2022-06-28T06:46:06Z-
dc.date.issued2019
dc.identifier.urihttp://repo.lib.jfn.ac.lk/ujrr/handle/123456789/4300-
dc.description.abstractIn order to overcome the problem of multicollinearity in logistic regres sion, several researchers proposed alternative estimators when exact linear restrictions are available in addition to sample model. However, in practical situations the linear restrictions are not always exact and mostly their nature is stochastic. In this paper, we propose a new estimator called stochastic restricted Liu maximum likelihood estimator (SRLMLE) by incorporating Liu estimator to the logistic regression model when the linear restrictions are stochastic. Moreover, the conditions for superiority of SRLMLE over the maximum likelihood estimator (MLE), stochastic restricted maximum like lihood estimator (SRMLE) and restricted Liu logistic estimator (RLLE) are derived with respect to mean square error criterion. Finally, the performance of the new esti mator over MLE, LLE, SRMLE and RLLE is investigated in the sense of scalar mean squared error by conducting a Monte Carlo simulation and using a numerical example.en_US
dc.language.isoenen_US
dc.publisherUniversity of Jaffnaen_US
dc.subjectlogistic regressionen_US
dc.subjectMulticollinearityen_US
dc.subjectliu estimatoren_US
dc.subjectStochastic restricted Liu maximum likelihood estimatoren_US
dc.subjectStochastic linear restrictionsen_US
dc.titleLogistic Liu Estimator under stochastic linear restrictionsen_US
dc.typeArticleen_US
Appears in Collections:Mathematics and Statistics

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