On data-based estimation of possibility distributions

In this paper, we show how a possibilistic description of uncertainty arises very naturally in statistical data analysis. In combination with recent results in inverse uncertainty propagation and the consistent aggregation of marginal possibility distributions, this estimation procedure enables a ve...

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Bibliographic Details
Published in:Fuzzy sets and systems 2020-11, Vol.399, p.77-94
Main Authors: Hose, Dominik, Hanss, Michael
Format: Article
Language:English
Subjects:
Fuzzy measurement equations
Fuzzy membership estimation
Possibilistic statistics
Possibility distribution estimation
Probability-possibility consistency
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Summary:In this paper, we show how a possibilistic description of uncertainty arises very naturally in statistical data analysis. In combination with recent results in inverse uncertainty propagation and the consistent aggregation of marginal possibility distributions, this estimation procedure enables a very general approach to possibilistic identification problems in the framework of imprecise probabilities, i.e. the non-parametric estimation of possibility distributions of uncertain variables from data with a clear interpretation.
ISSN:0165-0114
1872-6801
DOI:10.1016/j.fss.2020.03.017