Article ID Journal Published Year Pages File Type
393320 Information Sciences 2014 13 Pages PDF
Abstract

One of the key problems in statistics is to get information about the form of the population from which a sample is drawn. To check compatibility of a set of observed values with a presumed distribution one can apply various, so called, goodness-of-fit tests. It seems that the goodness-of-fit testing problem becomes much more complicated in the presence of imprecise observations. Actually, although many statistical procedure dedicated for specified types of distributions were generalized to fuzzy environment, still there are not too many tools that help under fuzzy data from the unknown distribution. Therefore, in the paper we suggest how to generalize the well-known one-sample goodness-of-fit tests based on the empirical distribution function, like the Kolmogorov test, the Cramér-von Mises test or the Anderson-Darling test, for fuzzy data.

Related Topics
Physical Sciences and Engineering Computer Science Artificial Intelligence
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