Testing statistical hypotheses with vague data
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2021, Applied Soft ComputingCitation Excerpt :In order to achieve this goal, many authors suggest to use specific defuzzification approaches (see the second defuzzification step in Fig. 2, violet-colored) like Fuzzy tests (with or without steps of defuzzification) are mostly natural generalizations of common statistical tests, that is, if hypotheses and data are crisp, one gets a classical statistical test with a binary (or trivalent) decision [99]. In the following, we identify previous application areas of fuzzy hypothesis testing, that is, we answer research question Q7.
Fuzzy hypothesis testing for a population proportion based on set-valued information
2020, Fuzzy Sets and SystemsCitation Excerpt :A recent paper by [23], that considers fuzzy p-values in the context of fuzzy hypothesis testing, illustrates this problem and provides some possible calculations and interpretations of the fuzzy p-value, in particular in relation to [8], [22], [5]. Further, the papers of [33], [35], [32], [16], [17], [14], [8], [5], [10], [30], [6], [23] consider crisp hypotheses with fuzzy, interval-valued or imprecise data, while the papers of [39], [40], and [26], [27], [28] use fuzzy hypotheses with crisp or fuzzy data. Some of these studies are rather focused on a general structure of a test and do not provide concrete tests for practical applications.