Analysis of Variance for Fuzzy Data Based on Permutation Method

This paper deals with analysis of variance with fuzzy data (ANOVAF) based on permutation method. The permutation method is a nonparametric method introduced by Heap and Johnson for the data when the normal distribution cannot be assumed. We proposed two different approaches to test hypothesis of fuz...

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Bibliographic Details
Published inINTERNATIONAL JOURNAL of FUZZY LOGIC and INTELLIGENT SYSTEMS Vol. 17; no. 1; pp. 43 - 50
Main Authors Lee, Woo-Joo, Jung, Hye-Young, Yoon, Jin Hee, Choi, Seung Hoe
Format Journal Article
LanguageEnglish
Published 한국지능시스템학회 31.03.2017
Subjects
전기공학
Permutation method
Fuzzy number
ANOVA
Monte Carlo simulation
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Summary:This paper deals with analysis of variance with fuzzy data (ANOVAF) based on permutation method. The permutation method is a nonparametric method introduced by Heap and Johnson for the data when the normal distribution cannot be assumed. We proposed two different approaches to test hypothesis of fuzzy means using the empirical distribution. To compare the results, several distances are considered especially using -distance. Applying Monte Carlo simulation, it is confirmed through the numerical examples that the significant probability (^p-value) get approached true parameter (p-value) regardless of distances or testing method based on proposed method. In addition, the number of permutation samples required is determined in the example to satisfy specified given accuracy. KCI Citation Count: 1
Bibliography:G704-001602.2017.17.1.003
ISSN:1598-2645
2093-744X
DOI:10.5391/IJFIS.2017.17.1.43