On the formalization of fuzzy random variables
Information Sciences: an International Journal - Fuzzy random variables
Two-sample hypothesis tests of means of a fuzzy random variable
Information Sciences: an International Journal - Fuzzy random variables
Some complete metrics on spaces of fuzzy subsets
Fuzzy Sets and Systems - Fuzzy intervals
Tools for fuzzy random variables: Embeddings and measurabilities
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
An improvement of a comparison of experiments in statistical decision problems with fuzzy utilities
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
IEEE Transactions on Fuzzy Systems
Triangular fuzzification of random variables and power of distribution tests: Empirical discussion
Computational Statistics & Data Analysis
Fuzzy Sets and Systems
Management of uncertainty in Statistical Reasoning: The case of Regression Analysis
International Journal of Approximate Reasoning
Estimation of a simple linear regression model for fuzzy random variables
Fuzzy Sets and Systems
Simulation of fuzzy random variables
Information Sciences: an International Journal
Multi-sample test-based clustering for fuzzy random variables
International Journal of Approximate Reasoning
Information Sciences: an International Journal
The fuzzy approach to statistical analysis
Computational Statistics & Data Analysis
Tools for fuzzy random variables: Embeddings and measurabilities
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
Fuzzy data treated as functional data: A one-way ANOVA test approach
Computational Statistics & Data Analysis
K-sample tests for equality of variances of random fuzzy sets
Computational Statistics & Data Analysis
The median of a random fuzzy number. The 1-norm distance approach
Fuzzy Sets and Systems
Bootstrap confidence sets for the Aumann mean of a random closed set
Computational Statistics & Data Analysis
Hi-index | 0.03 |
A bootstrap approach to the multi-sample test of means for imprecisely valued sample data is introduced. For this purpose imprecise data are modelled in terms of fuzzy values. Populations are identified with fuzzy-valued random elements, often referred to in the literature as fuzzy random variables. An example illustrates the use of the suggested method. Finally, the adequacy of the bootstrap approach to test the multi-sample hypothesis of means is discussed through a simulation comparative study.