Fuzzy Statistics

Título:

Fuzzy Statistics

ISBN:

9783540399193

Autor personal:

Buckley, James J. author.

Edición:

1st ed. 2004.

PRODUCTION_INFO:

Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2004.

Descripción física:

XI, 168 p. online resource.

Serie:

Studies in Fuzziness and Soft Computing, 149

Contenido:

Fuzzy Sets -- Estimate ?, Variance Known -- Estimate ?, Variance Unknown -- Estimate p, Binomial Population -- Estimate ?2 from a Normal Population -- Estimate µ 1 - µ 2, Variances Known -- Estimate ? 1 - ? 2, Variances Unknown -- Estimate d =? 1 - ? 2, Matched Pairs -- Estimate p 1 - p 2, Binomial Populations -- Estimate ? 1 2 /? 2 2 , Normal Populations -- Tests on µ, Variance Known -- Tests on µ, Variance Unknown -- Tests on p for a Binomial Population -- Tests on ? 2, Normal Population -- Tests ? 1 verses ? 2, Variances Known -- Test ? 1 verses ? 2, Variances Unknown -- Test p 1 = p 2, Binomial Populations -- Test d = µ 1 - µ 2 , Matched Pairs -- Test ? 1 2 verses ? 2 2 , Normal Populations -- Fuzzy Correlation -- Estimation in Simple Linear Regression -- Fuzzy Prediction in Linear Regression -- Hypothesis Testing in Regression -- Estimation in Multiple Regression -- Fuzzy Prediction in Regression -- Hypothesis Testing in Regression -- Summary and Questions -- Maple Commands.

Síntesis:

This monograph introduces elementary fuzzy statistics based on crisp (non-fuzzy) data. In the introductory chapters the book presents a very readable survey of fuzzy sets including fuzzy arithmetic and fuzzy functions. The book develops fuzzy estimation and demonstrates the construction of fuzzy estimators for various important and special cases of variance, mean and distribution functions. It is shown how to use fuzzy estimators in hypothesis testing and regression, which leads to a comprehensive presentation of fuzzy hypothesis testing and fuzzy regression as well as fuzzy prediction.

Término de la materia:

Engineering.

Probabilities.

Engineering mathematics.

Engineering -- Data processing.

Artificial intelligence.

Statistics .

Dynamics.

Nonlinear theories.

Technology and Engineering.

Probability Theory.

Mathematical and Computational Engineering Applications.