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Identifying the distribution difference between two populations of fuzzy data based on a nonparametric statistical method
Author(s) -
Lin PeiChun,
Watada Junzo,
Wu Berlin
Publication year - 2013
Publication title -
ieej transactions on electrical and electronic engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.254
H-Index - 30
eISSN - 1931-4981
pISSN - 1931-4973
DOI - 10.1002/tee.21901
Subject(s) - nonparametric statistics , kolmogorov–smirnov test , empirical distribution function , fuzzy logic , mathematics , statistics , sample (material) , statistical hypothesis testing , data mining , computer science , artificial intelligence , chemistry , chromatography
Nonparametric statistical tests are a distribution‐free method without any assumption that data are drawn from a particular probability distribution. In this paper, to identify the distribution difference between two populations of fuzzy data, we derive a function that can describe continuous fuzzy data. In particular, the Kolmogorov–Smirnov two‐sample test is used for distinguishing two populations of fuzzy data. Empirical studies illustrate that the Kolmogorov–Smirnov two‐sample test enables us to judge whether two independent samples of continuous fuzzy data are derived from the same population. The results show that the proposed function is successful in distinguishing two populations of continuous fuzzy data and useful in various applications. © 2013 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.