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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:   (1210 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]   (671 Downloads)    
Type of Study: Research | Subject: Special
Received: 2021/10/17 | Accepted: 2022/03/30 | Published: 2022/09/8
1. صانعی طبس، م. ( ١٣٩۴). اصل ماکسیمم آنتروپی تعمیم یافته مرتبه تعمیم یافته. رساله دکتری آمار ریاضی، دانشکده علوم ریاضی، دانشگاه فردوسی مشهد.
2. [2] Ayusuk, A. and Autchariyapanitkul, K. (2017). Factors Influencing Tourism Demand to Revisit Pha Ngan Island Using Generalized Maximum Entropy. Thai Journal of Mathematics, 187-196.
3. [3] Ciavolino, E. and Calcagni, A. (2016). A Generalized Maximum Entropy (GME) estimation approach to fuzzy regression model. 38, 51-63. [DOI:10.1016/j.asoc.2015.08.061]
4. [4] Fisher, R. A. (1912). On an absolute criterion for fitting frequency curves. Messenger of Mathematics, 41, 155-160.
5. [5] Fisher, R. A. (1922). On the mathematical foundations of theoretical statistics. Philosophical Transactions of the Royal Society of London Series A, 222, 309-368. [DOI:10.1098/rsta.1922.0009]
6. [6] Golan, A. (2002). Information and Entropy Econometrics Editor's View. Journal of Econometrics, 107, 1-15. [DOI:10.1016/S0304-4076(01)00110-5]
7. [7] Golan, A. (2008).Information and Entropy EconometricsA Review and Synthesis. New York, Now Publishers.
8. [8] Golan, A., Judge, G. and M.Perloff, J. (1996). A maximum entropy approach to recovering information from multinomial response data. Journal of the American Statistical Association, 91, 841-853. [DOI:10.2307/2291679]
9. [9] Golan, A., Moretti, E. and Perloff, J. M. (2001). A Small Sample Estimator for the SampleSelection Model. CUDARE Working Papers in University of California, Berkeley.
10. [10] Judge, G. G. and Golan, A. (1992). Recovering information in the case of ill-posed inverse problems with noise. Mimo Department Of Agricultural And Natural Resources, University of California, Berekeley, CA.
11. [11] Maneejuk, P. (2021). On regularization of generalized maximum entropy for linear models. Soft Computing, 25, 7867-7875. [DOI:10.1007/s00500-021-05805-2]
12. [12] Mittelhammer, R. C., Judge, G. G. and Miller, D. J. (2000). Econometric Foundations. New York, Cambridge University Press.
13. [13] Pearson, K. (1894). Contribution to the mathematical theory of evolution. Philosophical Transactions of the Royal Society of London Series A, 185, 71-110. [DOI:10.1098/rsta.1894.0003]
14. [14] Pearson, K. (1900). On the criterion that a given system of deviations from the probable in the case of a correlated system of variables is such that it can be reasonably supposed to have arisen from random sampling. Philosophical Magazine Series, 50, 157-175. [DOI:10.1080/14786440009463897]
15. [15] Pukelsheim, F. (1994). The three-sigma Rule. The American Statistician, 48, 88-91. [DOI:10.1080/00031305.1994.10476030]
16. [16] Song, H. and Witt, S. F. (2000). Tourism demand modelling and forecasting: Modern econometric approaches. Routledge.
17. [17] Sriboonchitta, S., Liu, J. and Sirisrisakulchai. (2015). Willingness-to-pay estimation using generalized maximum entropy: A case study, International Journal of Approximate Reasoning, 60, 1-7. [DOI:10.1016/j.ijar.2015.02.003]
18. [18] Thi Binh An, D., Tsuchida, J. and Yadohisa, H. (2021). K-means generalized maximum entropy estimation for structural equation modeling. Behaviormetrika, 48, 103-115. [DOI:10.1007/s41237-020-00118-4]
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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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Volume 26, Issue 2 (3-2022) Back to browse issues page
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