Parameter Estimation for Binary Logistic Regression Using Different Iterative Methods.
Keywords:Binary Logistic Regression, Maximum Likelihood Estimation, NRM, Modified NRM
Logistic Regression Analysis describes how a response variable having two or more categories is associated with a set of predictor variables (continuous or categorical) through a probability function. When the response variable is with only two categories a Binary Logistic Regression Model is the most widely used approach. The main deficiency with this method is in estimating logistic parameters numerically by applying Maximum Likelihood Estimation using Newton Raphson Method. In this paper, in order to improve the efficiency of the parameter estimates, four different modifications D-B-N; C-M-J; A-C-T; and L-W-W-Z, for NRM are introduced; each is an iterative method based on NRM. To specify the efficiency of these approaches, based on the number of iterations, all these procedures are compared with each other and then with NRM to identify the most efficient one. Finally, practical implementations for these procedures are given.
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