Abstract
Fuzzy modeling is a methodology that works with partial truths: it can answer questions to which the answers are “yes” and “no” at different times or partly “yes” and “no” at the same time. It can be used to match any type of data, particularly incomplete and imprecise data, and it is able to improve precision of such data. It can be applied with any type of statistical distribution and it is, particularly, suitable for uncommon and unexpected non linear relationships.
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Cleophas, T.J., Zwinderman, A.H. (2012). Fuzzy Modeling for Imprecise and Incomplete Data . In: Statistical Analysis of Clinical Data on a Pocket Calculator, Part 2. SpringerBriefs in Statistics. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-4704-3_10
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DOI: https://doi.org/10.1007/978-94-007-4704-3_10
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Publisher Name: Springer, Dordrecht
Print ISBN: 978-94-007-4703-6
Online ISBN: 978-94-007-4704-3
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