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Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2013 Vol.3(1): 21-23 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2013.V3.265

Fuzzy Bootstrap Test for the Mean and Variance with Dp,q-Distance

Bahram Sadeghpour Gildeh and Sedigheh Rahimpour
Abstract—Testing statistical hypothesis is a main topic in statistical inference. In this paper, we consider the problem of testing a simple hypothesis about the mean and variance of a fuzzy random variable with the help of Dp,q-distance. Concerning the hypothesis testing, the bootstrap techniques have empirically shown to be efficient and powerful. By means of simulation and some examples we show that the bootstrap method is a powerful tool in the statistical hypothesis testing about the parameters of fuzzy random variables.

Index Terms—Bootstrap, Dp,q-distance, fuzzy random variable, testing of hypothesis.

The authors are with Department of Statistics, University of Mazandaran, Babolsar, Iran (e-mail: sadeghpour@umz.ac.ir; rahimpour.s@gmail.com).

[PDF]

Cite:Bahram Sadeghpour Gildeh and Sedigheh Rahimpour, "Fuzzy Bootstrap Test for the Mean and Variance with Dp,q-Distance," International Journal of Machine Learning and Computing vol. 3, no. 1, pp. 21-23, 2013.

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