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:: Volume 26, Issue 2 (3-2022) ::
Andishe 2022, 26(2): 1-8 Back to browse issues page
Estimation of Logistic Regression Model Parameters Using Generalized Maximum Entropy
Mahsa Markani, Manije Sanei Tabas, Habib Naderi *, Hamed Ahmadzadeh, Javad Jamalzadeh
University of Sistan and Baluchestan
Abstract:   (743 Views)

‎When working on a set of regression data‎, ‎the situation arises that this data‎

‎It limits us‎, ‎in other words‎, ‎the data does not meet a set of requirements‎. ‎The generalized entropy method is able to estimate the model parameters‎ ‎Regression is without applying any conditions on the error probability distribution‎. ‎This method even in cases where the problem‎ ‎Too poorly designed (for example when sample size is too small‎, ‎or data that has alignment‎

‎They are high and‎ .‎..) is also capable. ‎Therefore‎, ‎the purpose of this study is to estimate the parameters of the logistic regression model using the generalized entropy of the maximum‎. ‎A random sample of bank customers was collected and in this study‎, ‎statistical work and were performed to estimate the model parameters from the binary logistic regression model using two methods maximum generalized entropy (GME) and maximum likelihood (ML)‎. ‎Finally‎, ‎two methods were performed‎. ‎We compare the mentioned‎. ‎Based on the accuracy of MSE criteria to predict customer demand for long-term account opening obtained from logistic regression using both GME and ML methods‎, ‎the GME method was finally more accurate than the ml method‎.

Keywords: Entropy, Generalized maximum entropy, Logistic regression, Logit, Maximum likelihood
Full-Text [PDF 196 kb]   (387 Downloads)    
Type of Study: Research | Subject: Special
Received: 2021/10/17 | Accepted: 2022/03/30 | Published: 2022/09/8
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Markani M, Sanei Tabas M, Naderi H, Ahmadzadeh H, Jamalzadeh J. Estimation of Logistic Regression Model Parameters Using Generalized Maximum Entropy. Andishe 2022; 26 (2) :1-8
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