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:: Volume 3, Issue 1 (9-2009) ::
JSS 2009, 3(1): 17-30 Back to browse issues page
Parameter Estimation for Logistic Regression Model Constructed by Evolutionary Product Unit Neural Networks
Maryam Torkzadeh * , Soroush Alimoradi
Abstract:   (4732 Views)
One of the tools for determining nonlinear effects and interactions between the explanatory variables in a logistic regression model is using of evolutionary product unit neural networks. To estimate the model parameters constructed by this method, a combination of evolutionary algorithms and classical optimization tools is used. In this paper, we change the structure of neural networks in the form that all model parameters can be estimated by using an evolutionary algorithms causes a model that is Akaike information criterion is better than conventional logisti model Akaike information criterion, but using the combination method gives the best model.
Keywords: Logistic regression, Neural networks, Evolutionary algorithm
Full-Text [PDF 540 kb]   (1007 Downloads)    
Type of Study: Applied | Subject: Statistical Inference
Received: 2018/11/16 | Accepted: 2018/11/16 | Published: 2018/11/16
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Torkzadeh M, Alimoradi S. Parameter Estimation for Logistic Regression Model Constructed by Evolutionary Product Unit Neural Networks. JSS 2009; 3 (1) :17-30
URL: http://jss.irstat.ir/article-1-638-en.html


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Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Volume 3, Issue 1 (9-2009) Back to browse issues page
مجله علوم آماری – نشریه علمی پژوهشی انجمن آمار ایران Journal of Statistical Sciences

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