Properties of the Maximum Likelihood Estimates and Bias Reduction for Logistic Regression Model - Open Access Library Journal

OALibJ > Vol.4 No.5, May 2017

Open Access Library Journal

Volume 4, Issue 5 (May 2017)

ISSN Print: 2333-9705 ISSN Online: 2333-9721

Google-based Impact Factor: 1.18 Citations

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

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DOI: 10.4236/oalib.1103625 1,618 Downloads 4,316 Views Citations

Author(s)

Nuri H. Salem Badi

Affiliation(s)

Mathematics Department, Faculty of Art and Science—Alabyar, University of Benghazi, Benghazi, Libya.

ABSTRACT

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.

KEYWORDS

Logistic Regression Model, Maximum Likelihood Method, Convergence Problems, Bias Reduction Technique

Share and Cite:

Badi, N. (2017) Properties of the Maximum Likelihood Estimates and Bias Reduction for Logistic Regression Model. Open Access Library Journal, 4, 1-12. doi: 10.4236/oalib.1103625.

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