Book description
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.
