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Nonparametric fuzzy hypothesis testing for quantiles applied to clinical characteristics of COVID‐19
Author(s) -
Chukhrova Nataliya,
Johannssen Arne
Publication year - 2021
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.22407
Subject(s) - fuzzy logic , quantile , probabilistic logic , sign test , generality , sign (mathematics) , nonparametric statistics , statistical hypothesis testing , test (biology) , computer science , data mining , population , artificial intelligence , computational intelligence , machine learning , mathematics , econometrics , statistics , psychology , mathematical analysis , paleontology , demography , sociology , wilcoxon signed rank test , psychotherapist , biology , mann–whitney u test
Abstract The sign test is one of the most popular nonparametric tests for location problems and allows testing for any quantile of a population. However, the common sign test has serious drawbacks such as loss of information by considering solely signs of observations but not their magnitudes, various problems related to handling of ties in the data, and the lack of embedding uncertainty regarding the fraction of underlying quantile. To address these issues, we present an extended sign test based on fuzzy categories and fuzzy formulated hypotheses that improves the generality, versatility, and practicability of the common sign test. This generalized test procedure is neat in theory and practice and avoids disadvantages that are often associated with fuzzy tests (e.g., a considerably higher complexity of the underlying model, a fuzzy test decision, and a possibilistic instead of a probabilistic interpretation of test results). In addition, we perform a comprehensive case study on COVID‐19 in HIV‐infected individuals with a focus on human body temperature and related measurement problems. The results of the study clearly indicate that fuzzy categories and fuzzy hypotheses improve the performance of the sign test.