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Urgan, N.N. and Tez, M. (2008) Liu Estimator in Logistic Regression When the Data Are Collinear. International Conference on Continuous Optimization and Knowledge-Based Technologies, Linthuania, Selected Papers, Vilnius, 323-327.

has been cited by the following article:

• TITLE: Stochastic Restricted Maximum Likelihood Estimator in Logistic Regression Model

  AUTHORS: Varathan Nagarajah, Pushpakanthie Wijekoon

  KEYWORDS: Logistic Regression, Multicollinearity, Stochastic Restricted Maximum Likelihood Estimator, Scalar Mean Squared Error

  JOURNAL NAME: Open Journal of Statistics, Vol.5 No.7, December 30, 2015

  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).