Open Access Open Access  Restricted Access Subscription or Fee Access

Alternative Estimators in Logit Model in The Presence of Multicollinearity and Heteroscedasticity with A Stochastic Linear Restricted

Saja Mohammad Hussein

Abstract



We propose grafting the maximum likelihood estimator (ML)for logit model into the mixed estimator (ME) and stochastic restricted ridge regression estimator (SRRR) for a linear model. To obtain estimators that can apply to models in which the dependent variable is binary in the presence of multicollinearity problem in the case of the heteroscedasticity of error term when stochastic linear restrictions are assumed to hold. A mean square error (MSE) is used to compare the performance of the proposed estimators through a simulation study and investigate The behavior of these estimators using some Ridge parameters.

Full Text: PDF


Disclaimer/Regarding indexing issue:

We have provided the online access of all issues and papers to the indexing agencies (as given on journal web site). It’s depend on indexing agencies when, how and what manner they can index or not. Hence, we like to inform that on the basis of earlier indexing, we can’t predict the today or future indexing policy of third party (i.e. indexing agencies) as they have right to discontinue any journal at any time without prior information to the journal. So, please neither sends any question nor expects any answer from us on the behalf of third party i.e. indexing agencies.Hence, we will not issue any certificate or letter for indexing issue. Our role is just to provide the online access to them. So we do properly this and one can visit indexing agencies website to get the authentic information.