A Comparison of Two Group Classification Approaches to Fat-tailed and Skewed Data

作者：Kardiyen Filiz^*; Olmus Hulya

来源：Communications in Statistics - Simulation and Computation, 2016, 45(1): 17-32.

DOI：10.1080/03610918.2013.849737

• Fat-tailed data
• Skewed data
• Stable distribution
• Two-group classification methods

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.

• 出版日期2016