Fuzzy hypothesis testing: Systematic review and bibliography

In statistical inference hypotheses related to different kinds of phenomena are formulated, and then data are collected and analyzed, which either confirm or falsify these hypotheses. Considering traditional statistics, in the underlying models hypotheses and sample data should be well defined. However, these models are often inadequate with regard to real-life problems, as theoretical specifications and observed information are frequently imprecise, vague, incomplete, qualitative, linguistic or noisy. To relax this rigidity, numerous researchers have proposed modifications and extensions of statistical inference approaches with the help of concepts of fuzzy statistics. In the meantime there are many papers on the topic of hypothesis testing in fuzzy environments, especially based on fuzzy hypotheses and/or by using fuzzy data. In order to structure this variety of contributions, proposals and applications, we give a comprehensive systematic review in this paper and offer a bibliography on fuzzy hypothesis testing. The paper seeks to consolidate the topic of fuzzy hypothesis testing with the purpose of supporting new researchers in this field and highlighting potential directions for future research. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

In statistical inference, researchers have proposed modifications and extensions using concepts of fuzzy statistics to deal with real-life problems that may involve imprecise or incomplete data. Many papers have explored hypothesis testing in fuzzy environments, focusing on fuzzy hypotheses and data. This paper provides a systematic review on fuzzy hypothesis testing to support new researchers and suggest future research directions.