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Orientador(es)
Resumo(s)
In this study, Monte Carlo simulation experiments were employed to examine the performance of four statistical two-group classification methods when the data distributions are skewed and misclassification costs are unequal, conditions frequently encountered in business and economic applications. The classification methods studied are linear and quadratic parametric, nearest neighbor and logistic regression methods. It was found that when skewness is moderate, the parametric methods tend to give best results. Depending on the specific data condition, when skewness is high, either the linear parametric, logistic regression, or the nearest-neighbor method gives the best results. When misclassification costs differ widely across groups, the linear parametric method is favored over the other methods for many of the data conditions studied.
Descrição
Palavras-chave
Classification analysis Discriminant analysis Nonparametric statistical methods
Contexto Educativo
Citação
Duarte Silva, A. P., Stam, A., Neter, J. (2002). The effects of misclassification costs and skewed distributions in two-group classification. Communications in Statistics - Simulation and Computation, 31(3), 401-423
Editora
Taylor & Francis