Open Journal of Statistics

Volume 5, Issue 7 (December 2015)

ISSN Print: 2161-718X   ISSN Online: 2161-7198

Google-based Impact Factor: 0.53  Citations  

Stochastic Restricted Maximum Likelihood Estimator in Logistic Regression Model

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DOI: 10.4236/ojs.2015.57082    4,218 Downloads   5,776 Views  Citations

ABSTRACT

In the presence of multicollinearity in logistic regression, the variance of the Maximum Likelihood Estimator (MLE) becomes inflated. Siray et al. (2015) [1] proposed a restricted Liu estimator in logistic regression model with exact linear restrictions. However, there are some situations, where the linear restrictions are stochastic. In this paper, we propose a Stochastic Restricted Maximum Likelihood Estimator (SRMLE) for the logistic regression model with stochastic linear restrictions to overcome this issue. Moreover, a Monte Carlo simulation is conducted for comparing the performances of the MLE, Restricted Maximum Likelihood Estimator (RMLE), Ridge Type Logistic Estimator(LRE), Liu Type Logistic Estimator(LLE), and SRMLE for the logistic regression model by using Scalar Mean Squared Error (SMSE).

Share and Cite:

Nagarajah, V. and Wijekoon, P. (2015) Stochastic Restricted Maximum Likelihood Estimator in Logistic Regression Model. Open Journal of Statistics, 5, 837-851. doi: 10.4236/ojs.2015.57082.

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