Statistical inference about the means of fuzzy random variables: Applications to the analysis of fuzzy- and real-valued data
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Simplicial depths for fuzzy random variables
2023, Fuzzy Sets and SystemsStatistical depth for fuzzy sets
2022, Fuzzy Sets and SystemsFuzzy hypothesis testing: Systematic review and bibliography
2021, Applied Soft ComputingCitation Excerpt :Interval-valued data mostly occurs due to insufficient technical conditions of measurements or when data is given in linguistic form, e.g. closed intervals that are not numeric [56]. Hybrid data is jointly affected by fuzziness (due to imprecision, partial ignorance, vagueness) and randomness (due to stochastic measurement or sampling errors) [57]. When exact boundaries of interval-valued data are inappropriate (e.g. in the case of fuzzy observations), non-precise data as generalization of real numbers and intervals can be consulted, i.e. non-precise data contains both fuzzy data and fuzzy intervals [58,59].
Clustering of fuzzy data and simultaneous feature selection: A model selection approach
2018, Fuzzy Sets and SystemsCitation Excerpt :This assumes the data under consideration is intrinsically fuzzy and follows mathematical formalism of fuzzy random variables, which are defined as mappings from a probability space to a fuzzy subset [2] with certain measurability properties. Some recent literature follows this interpretation of fuzzy data for estimation and hypothesis testing purposes [3,4]. Epistemic interpretation of fuzzy data: In this approach, the fuzzy numbers are assumed to “imperfectly specify a value that is existing and precise, but not measurable with exactitude under the given observation conditions” [1].
A hypothesis testing-based discussion on the sensitivity of means of fuzzy data with respect to data shape
2017, Fuzzy Sets and SystemsHypothesis testing for means in connection with fuzzy rating scale-based data: algorithms and applications
2016, European Journal of Operational ResearchCitation Excerpt :k-sample/ANOVA ones, to test the (‘two-sided’) null hypothesis of the equality of the population Aumann-type fuzzy means of k random fuzzy sets which are either independent (see Gil, Montenegro, González-Rodríguez, Colubi, and Casals, 2006; González-Rodríguez, Colubi, and Gil, 2012) or dependent (see Montenegro, López-García, Lubiano, and González-Rodríguez, 2009). For detailed reviews on the problem, one can see the paper by Colubi (2009), as well as the recent one by Blanco-Fernández et al. (2014a, 2014b). The above-mentioned testing methods have been developed to deal with general fuzzy-valued data, but most of the practical situations they apply to concern fuzzy number-valued data.