Abstract
In this paper the problem of real non-precise measurement data and their statistical analysis is considered. These data are usually not precise numbers or precise vectors, but more or less non-precise. This imprecision is different from statistical variation of data. It is also different from measurement errors. Special forms of fuzzy subsets of the real line can be used to describe non-precise data. Those non-precise numbers are characterized by so-called characterizing functions. Based on the description of non-precise data by characterizing functions, different methods from statistical inference can be generalized to the situation of non-precise measurements. The basic constructions of this generalized inference methods are given in this paper as well as some examples of how characterizing functions can be obtained for real non-precise observations.
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