This chapter introduces the underlying theory of Fuzzy Probability and Statistics related to the differences and similarities between discrete probability and possibility spaces. Fuzzy Probability Theory for Discrete Case starts with the fundamental tools to implement an immigration of crisp probability theory into fuzzy probability theory. Fuzzy random variables are the initial steps to develo...

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...

Min-independence has been proved to be a sufficient condition of a vector of fuzzy random variables to be a fuzzy random vector. The objective of this paper is to study further on the independence condition for fuzzy random vector based on continuous triangular norms. We first discuss measurability criteria for fuzzy random vector, and present two more new equivalent formulations of the measura...

In statistical inference, the point estimation problem is very crucial and has a wide range of applications. When, we deal with some concepts such as random variables, the parameters of interest and estimates may be reported/observed as imprecise. Therefore, the theory of fuzzy sets plays an important role in formulating such situations. In this paper, we rst recall the crisp uniformly minimum ...

The equilibrium measure is a natural extension of both probability and credibility measures. The convergence modes of random fuzzy variables with respect to equilibrium measure is an important issue for research. In this paper, we first introduce several convergence concepts for sequences of random fuzzy variables, including convergence in equilibrium measure and convergence in equilibrium dist...

Fuzzy random variables possess several interpretations. Historically, they were proposed either as a tool for handling linguistic label information in statistics or to represent uncertainty about classical random variables. Accordingly, there are two different approaches to the definition of the variance of a fuzzy random variable. In the first one, the variance of the fuzzy random variable is ...

in statistical inference, the point estimation problem is very crucial and has a wide range of applications. when, we deal with some concepts such as random variables, the parameters of interest and estimates may be reported/observed as imprecise. therefore, the theory of fuzzy sets plays an important role in formulating such situations. in this paper, we rst recall the crisp uniformly minimum ...

12:45 pm Lunch 02:30–03:30 pm A. COLUBI: Statistical inference about the means of fuzzy random variables: applications to the analysis of fuzzy-and real-valued data Estimation of a simple linear regression model for fuzzy random variables 04:00 pm Coffee break 04:30–05:00 pm A. WÜNSCHE: Asymptotic tests for the Aumann-expectation of fuzzy random variables 05:00–05:30 pm K. LENDELOVÁ, B. RIEČAN:...

The formulation of strong and weak laws of large numbers for fuzzy random variables based on variance is given. The concept of the variance of fuzzy random variables, introduced by K1 orner (Fuzzy Sets and Systems 92 (1997) 83) and by Feng (Fuzzy Sets and Systems 120 (2001) 487) demonstrates its e2ciency. c © 2002 Elsevier Science B.V. All rights reserved.