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Fuzzy statistische Entscheidungsfindung: Theorie und Anwendungen von Cengiz Kahraman (E-
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eBay-Artikelnr.:364699143973
Artikelmerkmale
- Artikelzustand
- ISBN-13
- 9783319817934
- Book Title
- Fuzzy Statistical Decision-Making
- ISBN
- 9783319817934
Über dieses Produkt
Product Identifiers
Publisher
Springer International Publishing A&G
ISBN-10
3319817930
ISBN-13
9783319817934
eBay Product ID (ePID)
8038597799
Product Key Features
Number of Pages
Xii, 356 Pages
Language
English
Publication Name
Fuzzy Statistical Decision-Making : Theory and Applications
Subject
Engineering (General), Intelligence (Ai) & Semantics, Probability & Statistics / General
Publication Year
2018
Type
Textbook
Subject Area
Mathematics, Computers, Technology & Engineering
Series
Studies in Fuzziness and Soft Computing Ser.
Format
Trade Paperback
Dimensions
Item Weight
196.2 Oz
Item Length
9.3 in
Item Width
6.1 in
Additional Product Features
Reviews
"The chapters are presented in intuitive, appealing manner and logical order, making the book as accessible to the widest possible readership. ... The book offers advanced methods in the field, useful practical examples and figures. The book contributes stimulating and substantial knowledge for the benefit of a host of research community and exhibits the use and practicality of the wonderful discipline statistical science. ... this book will be of interest to researchers in fuzzy statistics and related fields." (S. Ejaz Ahmed, Technometrics, Vol. 58, November, 2016)
Dewey Edition
23
Series Volume Number
343
Number of Volumes
1 vol.
Illustrated
Yes
Dewey Decimal
519.542
Table Of Content
Preface.- Fuzzy Statistical Decision Making.- Fuzzy Probability Theory I: Discrete Case.- Fuzzy Probability Theory II: Continuous Case.- On Fuzzy Bayesian Inference.- Fuzzy Central Tendency Measures.- Fuzzy Dispersion Measures.- Sufficiency, Completeness, and Unbiasedness based on Fuzzy Sample Space.- Fuzzy Confidence Regions.- Fuzzy Extensions of Confidence Intervals: Estimation for µ, σ2, and p .- Testing Fuzzy Hypotheses: A New p -value-based Approach.- Fuzzy Regression Analysis : An Actuarial Perspective.- Fuzzy Correlation and Fuzzy Non-Linear Regression Analysis.- Fuzzy Decision Trees.- Fuzzy Shewhart Control Charts.- Fuzzy EWMA and Fuzzy CUSUM Control Charts.- Linear Hypothesis Testing Based on Unbiased Fuzzy Estimators and Fuzzy Significance Level.- A Practical Application of Fuzzy Analysis of Variance in Agriculture.- A Survey of Fuzzy Data Mining Techniques.
Synopsis
Preface.- Fuzzy Statistical Decision Making.- Fuzzy Probability Theory I: Discrete Case.- Fuzzy Probability Theory II: Continuous Case.- On Fuzzy Bayesian Inference.- Fuzzy Central Tendency Measures.- Fuzzy Dispersion Measures.- Sufficiency, Completeness, and Unbiasedness based on Fuzzy Sample Space.- Fuzzy Confidence Regions.- Fuzzy Extensions of Confidence Intervals: Estimation for , 2, and p .- Testing Fuzzy Hypotheses: A New p -value-based Approach.- Fuzzy Regression Analysis: An Actuarial Perspective.- Fuzzy Correlation and Fuzzy Non-Linear Regression Analysis.- Fuzzy Decision Trees.- Fuzzy Shewhart Control Charts.- Fuzzy EWMA and Fuzzy CUSUM Control Charts.- Linear Hypothesis Testing Based on Unbiased Fuzzy Estimators and Fuzzy Significance Level.- A Practical Application of Fuzzy Analysis of Variance in Agriculture.- A Survey of Fuzzy Data Mining Techniques., This book offers a comprehensive reference guide to fuzzy statistics and fuzzy decision-making techniques. It provides readers with all the necessary tools for making statistical inference in the case of incomplete information or insufficient data, where classical statistics cannot be applied. The respective chapters, written by prominent researchers, explain a wealth of both basic and advanced concepts including: fuzzy probability distributions, fuzzy frequency distributions, fuzzy Bayesian inference, fuzzy mean, mode and median, fuzzy dispersion, fuzzy p-value, and many others. To foster a better understanding, all the chapters include relevant numerical examples or case studies. Taken together, they form an excellent reference guide for researchers, lecturers and postgraduate students pursuing research on fuzzy statistics. Moreover, by extending all the main aspects of classical statistical decision-making to its fuzzy counterpart, the book presents a dynamic snapshot of the field that is expected to stimulate new directions, ideas and developments.
LC Classification Number
Q342
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