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

KARDİYEN F., OLMUŞ H.

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, cilt.45, sa.1, ss.17-32, 2016 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 45 Sayı: 1
  • Basım Tarihi: 2016
  • Doi Numarası: 10.1080/03610918.2013.849737
  • Dergi Adı: COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.17-32
  • Anahtar Kelimeler: Fat-tailed data, Skewed data, Stable distribution, Two-group classification methods, LINEAR-PROGRAMMING MODELS, DISCRIMINANT-ANALYSIS, VARIABLES, RULE
  • Gazi Üniversitesi Adresli: Evet

Özet

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.