Publication Date



Technical Report: UTEP-07-21

Published in: Marek Reformat and Michael R. Berthold (eds.), Proceedings of the 26th International Conference of the North American Fuzzy Information Processing Society NAFIPS'2007, San Diego, California, June 24-27, 2007, pp. 560-565.


In traditional statistical analysis, if we know that the distribution is normal, then the most popular way to estimate its mean a and standard deviation s from the data sample x1,...,xn is to equate a and s to the arithmetic mean and sample standard deviation of this sample. After this equation, we get the cumulative distribution function F(x)=F0((x-a)/s) of the desired distribution.

In many practical situations, we only know intervals [xi] that contain the actual (unknown) values of xi or, more generally, a fuzzy number that describes xi. Different values of xi lead, in general, to different values of F(x). In this paper, we show how to compute, for every x, the resulting interval [F(x)] of possible values of F(x) - or the corresponding fuzzy numbers.