Statistical data are not always precise numbers, or vectors, or
categories. Real data are frequently what is called fuzzy. Examples
where this fuzziness is obvious are quality of life data,
environmental, biological, medical, sociological and economics
data. Also the results of measurements can be best described by
using fuzzy numbers and fuzzy vectors respectively.
Statistical analysis methods have to be adapted for the analysis
of fuzzy data. In this book, the foundations of the description of
fuzzy data are explained, including methods on how to obtain the
characterizing function of fuzzy measurement results. Furthermore,
statistical methods are then generalized to the analysis of fuzzy
data and fuzzy a-priori information.
Key Features:
* Provides basic methods for the mathematical description of
fuzzy data, as well as statistical methods that can be used to
analyze fuzzy data.
* Describes methods of increasing importance with applications in
areas such as environmental statistics and social science.
* Complements the theory with exercises and solutions and is
illustrated throughout with diagrams and examples.
* Explores areas such quantitative description of data
uncertainty and mathematical description of fuzzy data.
This work is aimed at statisticians working with fuzzy logic,
engineering statisticians, finance researchers, and environmental
statisticians. It is written for readers who are familiar with
elementary stochastic models and basic statistical methods.
Über den Autor
Reinhard Viertl is Professor of Applied Statistics at Vienna University of Technology.
Professor Viertl has been working on statistical analysis of fuzzy data for about 20 years. He is the author of various publications including 5 books and more than 100 papers.
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