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:: Volume 7, Issue 1 (9-2013) ::
J. of Stat. Sci. 2013, 7(1): 125-143 Back to browse issues page
Inverse Multiquadratic Functions as Nonlinear Effects in Logistic Regression Models
Arezou Mojiri , Soroush Alimoradi , Mohammadreza Ahmadzade
Abstract:   (11997 Views)
Logistic regression models in classification problems by assuming the linear effects of covariates is a modeling for class membership posterior probabilities. The main problem that includes nonlinear combinations of covariates is maximum likelihood estimation (MLE) of the model parameters. In recent investigations, an approach of solving this problem is combination of neural networks, evolutionary algorithms and MLE methods. In this paper, another type of radial basis functions, namely inverse multiquadratic functions and hybrid method, are considered for estimating the parameters of these models. The experimental results of comparing the proposed models show that the inverse multiquadratic functions compared to the Gaussian functions have better precision in classification problems.
Keywords: Classification, Logistic regression, Inverse multiquadratic functions, Evolutionary neural networks
Full-Text [PDF 663 kb]   (1514 Downloads)    
Type of Study: Applied | Subject: Applied Statistics
Received: 2013/09/29 | Accepted: 2013/12/29 | Published: 2013/12/29
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Mojiri A, Alimoradi S, Ahmadzade M. Inverse Multiquadratic Functions as Nonlinear Effects in Logistic Regression Models. J. of Stat. Sci.. 2013; 7 (1) :125-143
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Volume 7, Issue 1 (9-2013) Back to browse issues page
مجله علوم آماری – نشریه علمی پژوهشی انجمن آمار ایران Journal of Statistical Sciences
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