Unbiased Estimation For Logistic Regression

dc.authorid	TR46651	en_US
dc.contributor.author	Eyduran, Ecevit
dc.contributor.author	Ozdemir, Taner
dc.date.accessioned	2015-04-03T07:26:49Z
dc.date.available	2015-04-03T07:26:49Z
dc.date.issued	2007
dc.department	İstanbul Beykent Üniversitesi	en_US
dc.description.abstract	The aim of this study was to determine and reduce the problems that have been encountered in sparse data or separation. For this aim, Firth's Modified Score Procedure, which is more superior to Maximum Likelihood Estimation Method, were performed on a data set regarding psychology. Under some circumstances (sparse data or separation), maximum likelihood estimates of parameters are biased or infinite estimates. Using Firth's Modified Score Procedure or (Firth Type Estimates) may be thought as an originally approach which eliminates or shrinks the first order bias. As a result, it has been suggested in this article that Firth Type Estimates be a reliable method for eliminating separation.	en_US
dc.identifier.citation	Journal of Science and Technology 1 (2), 2007, 245-251	tr_TR
dc.identifier.issn	1307-3818
dc.language.iso	en	en_US
dc.publisher	Beykent Üniversitesi	tr_TR
dc.relation.publicationcategory	Makale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı	en_US
dc.subject	Bias shrinking	tr_TR
dc.subject	Firth Type Estimation	tr_TR
dc.subject	Sparse data	tr_TR
dc.subject	Separation	tr_TR
dc.title	Unbiased Estimation For Logistic Regression	en_US
dc.type	Article	en_US