Publication Date



Technical Report: UTEP-CS-07-16a

Published in: Hsiao-Fan Wang (ed.), Intelligent Data Analysis: Developing New Methodologies Through Pattern Discovery and Recovery, IGI Global, Hershey, Pennsylvania, 2008, pp. 18-44.


It is well known that in decision making under uncertainty, while we are guided by a general (and abstract) theory of probability and of statistical inference, each specific type of observed data requires its own analysis. Thus, while textbook techniques treat precisely observed data in multivariate analysis, there are many open research problems when data are censored (e.g., in medical or bio-statistics), missing, or partially observed (e.g., in bioinformatics). Data can be imprecise due to various reasons, e.g., due to fuzziness of linguistic data. Imprecise observed data are usually called {\it coarse data}. In this chapter, we consider coarse data which are both random and fuzzy.

Fuzziness is a form of imprecision often encountered in perception-based information. In order to develop statistical reference procedures based on such data, we need to model random fuzzy data as bona fide random elements, i.e., we need to place random fuzzy data completely within the rigorous theory of probability. This chapter presents the most general framework for random fuzzy data, namely the framework of random fuzzy sets. We also describe several applications of this framework.

tr07-16.pdf (290 kB)
Original file: CS-07-16