Abstract: The bootstrap is a simple and straightforward method for calculating approximated biases, standard deviations, confidence intervals, testing statistical hypotheses, and so forth, in almost any nonparametric estimation problem. In this paper we describe a bootstrap method for variance that is designed directly for hypothesis testing in case of fuzzy data based on Yao-Wu signed distance.

After introducing and developing fuzzy set theory, a lot of studies have been done to combine statistical methods and fuzzy set theory. This works, called fuzzy statistics, have been developed in some branches. In this article we review essential works on fuzzy estimation, fuzzy hypotheses testing, fuzzy regression, fuzzy Bayesian statistics, and some relevant fields. Zusammenfassung: Nach Entw...

The variance of a fuzzy random variable plays an important role as a measure of central tendency. Some of the main contributions in this topic are consolidated and discussed in this paper. In case of the hypothesis testing problem, bootstrap techniques (Efron and Tibshirani, 1993) have empirically been shown to be efficient and powerful. Algorithms to apply these techniques in practice and some...

Conventionally, we use a chi-square test of homogeneity to determine whether the cell probabilities of a multinomial are equal. However, this process of testing hypotheses is based on the assumption of two-valued logic. If we collect questionnaire data using fuzzy logic, i.e., we record the category data with memberships instead of with a 0-1 type, then the conventional test of goodness-of-fit ...

Hypothesis testing of population parameters plays an important role in statistical analysis. This work extends this topic to the fuzzy environment for wider application. Developing a method of testing fuzzy hypotheses using linguistic variables is of priority concern. The method is based on Zadeh’s extension principle: the fuzzy population mean in the null hypothesis is converted to fuzzy numbe...