Statistical Methods for Fuzzy Data

Beschreibung

Statistical data are not always precise numbers, or vectors, orcategories. Real data are frequently what is called fuzzy. Exampleswhere this fuzziness is obvious are quality of life data,environmental, biological, medical, sociological and economicsdata. Also the results of measurements can be best described byusing fuzzy numbers and fuzzy vectors respectively. Statistical analysis methods have to be adapted for the analysisof fuzzy data. In this book, the foundations of the description offuzzy data are explained, including methods on how to obtain thecharacterizing function of fuzzy measurement results. Furthermore,statistical methods are then generalized to the analysis of fuzzydata and fuzzy a-priori information. Key Features: * Provides basic methods for the mathematical description offuzzy data, as well as statistical methods that can be used toanalyze fuzzy data. * Describes methods of increasing importance with applications inareas such as environmental statistics and social science. * Complements the theory with exercises and solutions and isillustrated throughout with diagrams and examples. * Explores areas such quantitative description of datauncertainty and mathematical description of fuzzy data. This work is aimed at statisticians working with fuzzy logic,engineering statisticians, finance researchers, and environmentalstatisticians. It is written for readers who are familiar withelementary stochastic models and basic statistical methods.

Autor*in

Reinhard Viertl

Themen in »Statistical Methods for Fuzzy Data«

Engineering Statistics Fuzzy-Logik Statistics Statistics for Social Sciences Statistik Statistik in den Ingenieurwissenschaften Statistik in den Sozialwissenschaften Unscharfe Menge

Stimmen zu »Statistical Methods for Fuzzy Data«

"I recommend this book to anyone interested in exploringnew approaches to the extraction of information from novel datasources." (International Statistical Review,2012)
()

Details

Link teilen