Properties of the Maximum Likelihood Estimates and Bias Reduction for Logistic Regression Model

摘要

A frequent problem in estimating logistic regressionmodels is a failure of the likelihood maximization algorithm to converge.Although popular and extremely well established in bias correction for maximumlikelihood estimates of the parameters for logistic regression, the behaviourand properties of the maximum likelihood method are less investigated. The main aim of this paper is to examine the behaviourand properties of the parameters estimates methods with reduction technique. Wewill focus on a method used a modified score function to reduce the bias of themaximum likelihood estimates. We also present new and interesting examples bysimulation data with different cases of sample size and percentage of theprobability of outcome variable.