Identifying the distribution difference between two populations of fuzzy data based on a nonparametric statistical method

Pei Chun Lin, Junzo Watada, Berlin Wu

    Research output: Contribution to journalArticle

    8 Citations (Scopus)

    Abstract

    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.

    Original languageEnglish
    Pages (from-to)591-598
    Number of pages8
    JournalIEEJ Transactions on Electrical and Electronic Engineering
    Volume8
    Issue number6
    DOIs
    Publication statusPublished - 2013 Nov

    Fingerprint

    Statistical methods
    Statistical tests
    Probability distributions

    Keywords

    • Empirical distribution function
    • Fuzzy numbers
    • Fuzzy statistics and data analysis
    • Goodness-of-fit test
    • Kolmogorov-Smirnov two-sample test
    • Membership functions

    ASJC Scopus subject areas

    • Electrical and Electronic Engineering

    Cite this

    Identifying the distribution difference between two populations of fuzzy data based on a nonparametric statistical method. / Lin, Pei Chun; Watada, Junzo; Wu, Berlin.

    In: IEEJ Transactions on Electrical and Electronic Engineering, Vol. 8, No. 6, 11.2013, p. 591-598.

    Research output: Contribution to journalArticle

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