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June 2007 On Testing Hypothesis of Fuzzy Sample Mean
Berlin Wu, Shu-Kwang Chang
Japan J. Indust. Appl. Math. 24(2): 197-209 (June 2007).

Abstract

In many expositions of fuzzy methods, fuzzy techniques are described as an alternative to a more traditional statistical approach. In this paper, we present a class of fuzzy statistical decision process in which testing hypothesis can be naturally reformulated in terms of interval-valued statistics. We provide the definitions of fuzzy mean, fuzzy distance as well as investigation of their related properties. We also give some empirical examples to illustrate the techniques and to analyze fuzzy data. Empirical studies show that fuzzy hypothesis testing with soft computing for interval data are more realistic and reasonable in the social science research. Finally certain comments are suggested for the further studies. We hope that this reformation will make the corresponding fuzzy techniques more acceptable to researchers whose only experience is in using traditional statistical methods.

Citation

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Berlin Wu. Shu-Kwang Chang. "On Testing Hypothesis of Fuzzy Sample Mean." Japan J. Indust. Appl. Math. 24 (2) 197 - 209, June 2007.

Information

Published: June 2007
First available in Project Euclid: 17 December 2007

zbMATH: 1128.62061
MathSciNet: MR2338154

Rights: Copyright © 2007 The Japan Society for Industrial and Applied Mathematics

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Vol.24 • No. 2 • June 2007
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