A frequent problem in estimating logistic regression
models is a failure of the likelihood maximization algorithm to converge.
Although popular and extremely well established in bias correction for maximum
likelihood estimates of the parameters for logistic regression, the behaviour
and properties of the maximum likelihood method are less investigated. The main aim of this paper is to examine the behaviour
and properties of the parameters estimates methods with reduction technique. We
will focus on a method used a modified score function to reduce the bias of the
maximum likelihood estimates. We also present new and interesting examples by
simulation data with different cases of sample size and percentage of the
probability of outcome variable.
Cite this paper
Badi, N. H. S. (2017). Properties of the Maximum Likelihood Estimates and Bias Reduction for Logistic Regression Model. Open Access Library Journal, 4, e3625. doi: http://dx.doi.org/10.4236/oalib.1103625.
C.C., Rubin, D.B., Schenker, N., Schultz, B. and
Weidman, L. (1991) Multiple Imputation
of Industry and Occupation Codes in Census Public-Use Samples Using Bayesian
Logistic Regression. Journal of the American Statistical Association,
86, 68-78. https://doi.org/10.1080/01621459.1991.10475005