A Comparison of Two Group Classification Approaches to Fat-tailed and Skewed Data |
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Authors: | Filiz Kardiyen Hülya Olmuş |
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Institution: | Faculty of Sciences, Department of Statistics, Gazi University, Ankara, Turkey |
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Abstract: | The problem of two-group classification has implications in a number of fields, such as medicine, finance, and economics. This study aims to compare the methods of two-group classification. The minimum sum of deviations and linear programming model, linear discriminant analysis, quadratic discriminant analysis and logistic regression, multivariate analysis of variance (MANOVA) test-based classification and the unpooled T-square test-based classification methods, support vector machines and k-nearest neighbor methods, and combined classification method will be compared for data structures having fat-tail and/or skewness. The comparison has been carried out by using a simulation procedure designed for various stable distribution structures and sample sizes. |
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Keywords: | Fat-tailed data Skewed data Stable distribution Two-group classification methods |
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