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:: Volume 16, Issue 2 (3-2023) ::
JSS 2023, 16(2): 449-468 Back to browse issues page
Semiparametric Multinomial Logistic Regression Model to Classify‎ ‎Shape Data
Meisam Moghimbeygi *
Abstract:   (300 Views)

This article introduces a semiparametric multinomial logistic regression model to classify labeled configurations. In the regression model, the explanatory variable is the kernel function obtained using the power-divergence criterion. Also, the response variable was categorical and showed the class of each configuration. This semiparametric regression model is introduced based on distances defined in the shape space, and for this reason, the correct classification of shapes using this method has been improved compared to previous methods. ‎The performance of this model has been investigated in the comprehensive simulation study‎. ‎Two real datasets were analyzed using this article's method as an application‎. ‎Finally‎, ‎the method presented in this article was compared with the techniques introduced in the literature‎, ‎which shows the proper performance of this method in classifying configurations‎.

Article number: 11
Keywords: Logistic regresion, ‎S‎emiparametric regression, Shape data, ‎Classification
Full-Text [PDF 375 kb]   (195 Downloads)    
Type of Study: Research | Subject: General
Received: 2022/09/18 | Accepted: 2023/03/1 | Published: 2022/12/21
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Moghimbeygi M. Semiparametric Multinomial Logistic Regression Model to Classify‎ ‎Shape Data. JSS 2023; 16 (2) :449-468
URL: http://jss.irstat.ir/article-1-817-en.html

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Volume 16, Issue 2 (3-2023) Back to browse issues page
مجله علوم آماری – نشریه علمی پژوهشی انجمن آمار ایران Journal of Statistical Sciences

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