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

Pei Chun Lin; Junzo Watada; Berlin Wu

doi:10.1002/tee.21901

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

Pei Chun Lin^*, Junzo Watada, Berlin Wu

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

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 language	English
Pages (from-to)	591-598
Number of pages	8
Journal	IEEJ Transactions on Electrical and Electronic Engineering
Volume	8
Issue number	6
DOIs	https://doi.org/10.1002/tee.21901
Publication status	Published - 2013 Nov

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

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10.1002/tee.21901

Cite this

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

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.",

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

author = "Lin, {Pei Chun} and Junzo Watada and Berlin Wu",

year = "2013",

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doi = "10.1002/tee.21901",

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T1 - Identifying the distribution difference between two populations of fuzzy data based on a nonparametric statistical method

AU - Lin, Pei Chun

AU - Watada, Junzo

AU - Wu, Berlin

PY - 2013/11

Y1 - 2013/11

N2 - 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.

AB - 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.

KW - Empirical distribution function

KW - Fuzzy numbers

KW - Fuzzy statistics and data analysis

KW - Goodness-of-fit test

KW - Kolmogorov-Smirnov two-sample test

KW - Membership functions

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JF - IEEJ Transactions on Electrical and Electronic Engineering

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

Abstract

Keywords

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