Title: A methodology to analyze fuzzy data
Authors: Maria Angeles Gil - Universidad de Oviedo (Spain) [presenting]
Abstract: Fuzzy data are often used to model data associated with intrinsically imprecise-valued magnitudes/attributes (say perceived quality, satisfaction, attitude, and so on) in a random environment. The aim is to recall the required preliminary tools, and to present some of the already established statistical developments in connection with the central tendency/location and dispersion/scale of random fuzzy numbers. The estimation and testing methods about the population Aumann-type mean(s) are to be exposed. Some alternate robust location measures are introduced, their estimation is examined and their robustness is discussed. Regarding dispersion, the estimation and testing methods about the population Frechet-type variance(s) are to be described and some alternate robust scale measures are introduced, their estimation is examined and their robustness is discussed. Some of the presented methods will be illustrated by means of a real-life example. This example will serve to show the convenience of using the scale of fuzzy numbers instead of other scales like Likert-type ones or their fuzzy linguistic counterpart in dealing with data from these intrinsically imprecise-valued magnitudes/attributes. Related studies as well as some future directions will be shortly commented.