Clear and didactic book about fuzzy probabilities and statistics useful for students and researchers
This book combines material from our previous books FP (Fuzzy Probabilities: New Approach and Applications,Physica-Verlag, 2003) and FS (Fuzzy Statistics, Springer, 2004), plus has about one third new results. From FP we have material on basic fuzzy probability, discrete (fuzzy Poisson,binomial) and continuous (uniform, normal, exponential) fuzzy random variables. From FS we included chapters on fuzzy estimation and fuzzy hypothesis testing related to means, variances, proportions, correlation and regression. New material includes fuzzy estimators for arrival and service rates, and the uniform distribution, with applications in fuzzy queuing theory. Also, new to this book, is three chapters on fuzzy maximum entropy (imprecise side conditions) estimators producing fuzzy distributions and crisp discrete/continuous distributions. Other new results are: (1) two chapters on fuzzy ANOVA (one-way and two-way); (2) random fuzzy numbers with applications to fuzzy Monte Carlo studies; and (3) a fuzzy nonparametric estimator for the median.
Fuzzy Sets.- Fuzzy Probability Theory.- Discrete Fuzzy Random Variables.- Continuous Fuzzy Random Variables.- Estimate ?, Variance Known.- Estimate ?, Variance Unknown.- Estimate p, Binomial Population.- Estimate ?2 from a Normal Population.- Fuzzy Arrival/Service Rates.- Fuzzy Uniform.- Fuzzy Max Entropy Principle.- Max Entropy: Crisp Discrete Solutions.- Max Entropy: Crisp Continuous Solutions.- Tests on ?, Variance Known.- Tests on ?, Variance Unknown.- Tests on p for a Binomial Population.- Tests on ?2, Normal Population.- 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.- Fuzzy One-Way ANOVA.- Fuzzy Two-Way ANOVA.- Fuzzy Estimator for the Median.- Random Fuzzy Numbers.- Selected Maple/Solver Commands.- Summary and Future Research.
From the reviews:
"The reviewed book is an interesting and well organized monograph on fuzzy statistics which can be recommended for specialists and non-specialists in the field of fuzzy sets research." (Krzysztof Piasecki, Zentralblatt MATH, Vol. 1095 (21), 2006)
1.1 Introduction This book is written in the following divisions: (1) the introductory chapters consisting of Chapters 1 and 2; (2) introduction to fuzzy probability in Ch- ters3-5; (3)introductiontofuzzyestimationinChapters6-11; (4)fuzzy/crisp estimatorsofprobabilitydensity(mass)functionsbasedonafuzzymaximum entropyprincipleinChapters12-14; (5)introductiontofuzzyhypothesiste- ing in Chapters 15-18; (6) fuzzy correlation and regression in Chapters 19-25; (7) Chapters 26 and 27 are about a fuzzy ANOVA model; (8) a fuzzy esti- tor of the median in nonparametric statistics in Chapter 28; and (9) random fuzzy numbers with applications to Monte Carlo studies in Chapter 29. First we need to be familiar with fuzzy sets. All you need to know about fuzzy sets for this book comprises Chapter 2. For a beginning introduction to fuzzysetsandfuzzylogicsee[8]. Oneotheritemrelatingtofuzzysets,needed infuzzyhypothesistesting, isalsoinChapter2: howwewilldeterminewhich of the following three possibilities is trueM N or M? N,for two fuzzy numbers M, N. TheintroductiontofuzzyprobabilityinChapters3-5isbasedonthebook [1] and the reader is referred to that book for more information, especially applications. Whatisnewhereis: (1)usinganonlinearoptimizationprogram in Maple [13] to solve certain optimization problems in fuzzy probability, where previously we used a graphical method; and (2) a new algorithm, suitable for using only pencil and paper, for solving some restricted fuzzy arithmetic problems. The introduction to fuzzy estimation is based on the book [3] and we refer the interested reader to that book for more about fuzzy estimators.
From the reviews:
"The reviewed book is an interesting and well organized monograph on fuzzy statistics which can be recommended for specialists and non-specialists in the field of fuzzy sets research." (Krzysztof Piasecki, Zentralblatt MATH, Vol. 1095 (21), 2006)
Inhaltsverzeichnis
Fuzzy Sets.- Fuzzy Probability Theory.- Discrete Fuzzy Random Variables.- Continuous Fuzzy Random Variables.- Estimate ?, Variance Known.- Estimate ?, Variance Unknown.- Estimate p, Binomial Population.- Estimate ?2 from a Normal Population.- Fuzzy Arrival/Service Rates.- Fuzzy Uniform.- Fuzzy Max Entropy Principle.- Max Entropy: Crisp Discrete Solutions.- Max Entropy: Crisp Continuous Solutions.- Tests on ?, Variance Known.- Tests on ?, Variance Unknown.- Tests on p for a Binomial Population.- Tests on ?2, Normal Population.- 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.- Fuzzy One-Way ANOVA.- Fuzzy Two-Way ANOVA.- Fuzzy Estimator for the Median.- Random Fuzzy Numbers.- Selected Maple/Solver Commands.- Summary and Future Research.
Klappentext
This book combines material from our previous books FP (Fuzzy Probabilities: New Approach and Applications,Physica-Verlag, 2003) and FS (Fuzzy Statistics, Springer, 2004), plus has about one third new results. From FP we have material on basic fuzzy probability, discrete (fuzzy Poisson,binomial) and continuous (uniform, normal, exponential) fuzzy random variables. From FS we included chapters on fuzzy estimation and fuzzy hypothesis testing related to means, variances, proportions, correlation and regression. New material includes fuzzy estimators for arrival and service rates, and the uniform distribution, with applications in fuzzy queuing theory. Also, new to this book, is three chapters on fuzzy maximum entropy (imprecise side conditions) estimators producing fuzzy distributions and crisp discrete/continuous distributions. Other new results are: (1) two chapters on fuzzy ANOVA (one-way and two-way); (2) random fuzzy numbers with applications to fuzzy Monte Carlo studies; and (3) a fuzzy nonparametric estimator for the median.
Clear and didactic book about fuzzy probabilities and statistics useful for students and researchers
