Abstract: A fuzzy test for testing statistical hypotheses about an imprecise parameter is proposed for the case when the available data are also imprecise. The proposed method is based on the relationship between the acceptance region of statistical tests at level β and confidence intervals for the parameter of interest at confidence level 1 − β. First, a fuzzy confidence interval is constructe...

A novel approach is proposed for the problem of testing statistical hypotheses about the fuzzy mean of a fuzzy random variable.The concept of the (uniformly) most powerful test is extended to the (uniformly) most powerful fuzzy-valued test in which the test function is a fuzzy set representing the degrees of rejection and acceptance of the hypothesis of interest.For this purpose, the concepts o...

This work presents an approach to the automatic interpretation of the visual field to enable ophthalmology patients to be classified as glaucomatous and normal. The approach is based on a neuro-fuzzy system (NEFCLASS) that enables a set of rules to be learnt, with no a priori knowledge, and the fuzzy sets that form the rule antecedents to be tuned, on the basis of a set of training data. Anothe...

A classical parametric test can be extended to a fuzzy test, if the data are fuzzy. We show that three different ways of the extension are equivalent.

The objective of this research is to study various assessment methods used in modern test systems. Methods based on classical test theory and IRT (item response theory) have been considered in this paper. The approaches to improve the quality of assessment of the ability level as a function of the diversity of evaluation points have been proposed. Statistical analysis packages SPSS, the tests a...

In testing statistical hypotheses, as in other statistical problems, we may be confronted with fuzzy concepts. This paper deals with the problem of testing hypotheses, when the hypotheses are fuzzy and the data are crisp. We first give new definitions for notion of mass (density) probability function with fuzzy parameter, probability of type I and type II errors and then state and prove the seq...

This paper introduces an approximate fuzzy representation to FuzzyUCS, a Michigan-style Learning Fuzzy-Classifier System that evolves linguistic fuzzy rules, and studies whether the flexibility provided by the approximate representation results in a significant improvement of the accuracy of the models evolved by the system. We test Fuzzy-UCS with both approximate and linguistic representation ...