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
Information Sciences—Informatics and Computer Science: An International Journal
A fuzzy-based methodology for the analysis of diabetic neuropathy
Fuzzy Sets and Systems - Data bases and approximate reasoning
Regression with fuzzy random data
Computational Statistics & Data Analysis
Least squares estimation of a linear regression model with LR fuzzy response
Computational Statistics & Data Analysis
The fuzzy approach to statistical analysis
Computational Statistics & Data Analysis
Generalized theory of uncertainty (GTU)-principal concepts and ideas
Computational Statistics & Data Analysis
Tools for fuzzy random variables: Embeddings and measurabilities
Computational Statistics & Data Analysis
Bootstrap approach to the multi-sample test of means with imprecise data
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
Estimation of a simple linear regression model for fuzzy random variables
Fuzzy Sets and Systems
Fuzzy data treated as functional data: A one-way ANOVA test approach
Computational Statistics & Data Analysis
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A fuzzifying process of finitely valued random variables by means of triangular fuzzy sets is analyzed. Empirical studies show that if the random variable takes on a small number of different values, the one-sample test about the (fuzzy) mean of the fuzzified random variable is frequently more powerful than the classical test about the mean of the original random variable. This empirical conclusion is theoretically supported as follows: whenever the number of different values of a random variable X is up to 4, the mean of the fuzzified random variable captures the whole information on its distribution. As a consequence, the statistical test about the mean of the fuzzified random variable can be considered in fact as a goodness-of-fit test for the original random variable and, analogously, the J-sample test becomes a test for the equality of J distributions. Comparative simulation studies of these procedures with respect to other well-known methods are carried out. A real-life example illustrates the introduced methodology.