Fuzzy Statistical Decision-Making
Theory and Applications
Published on 26. July 2016
Book
Hardback
XII, 356 pages
978-3-319-39012-3 (ISBN)
Description
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.
Reviews / Votes
"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)More details
Series
Edition
1st ed. 2016
Language
English
Place of publication
Cham
Switzerland
Publishing group
Springer International Publishing
Target group
Professional and scholarly
Illustrations
79 s/w Abbildungen, 5 farbige Abbildungen
XII, 356 p. 84 illus., 5 illus. in color.
Dimensions
Height: 241 mm
Width: 160 mm
Thickness: 26 mm
Weight
717 gr
ISBN-13
978-3-319-39012-3 (9783319390123)
DOI
10.1007/978-3-319-39014-7
Schweitzer Classification
Other editions
Additional editions
Book
05/2018
Springer
€106.99
Shipment within 10-15 days
E-Book
07/2016
Springer
€96.29
Available for download
Persons
Prof. Kahraman received his BSc (1988), MSc (1990), and PhD (1996) degrees in Industrial Engineering from the Istanbul Technical University. His main research areas include engineering economics, quality management and control, statistical decision making, and fuzzy sets applications. He has published about 150 papers in international journals and more than 5 books with Springer. He has served as guest editor of many special issues of international journals and is presently the Head of the Industrial Engineering department of the Istanbul Technical University. Dr. Özgür Kabak received his BSc (2001), MSc (2003), and PhD (2008) degrees in Industrial Engineering from the Istanbul Technical University. He is currently Assistant Professor of Industrial Engineering in the same University. His main research areas are fuzzy decision making, mathematical programming and statistical decision making.
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, s 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.
based on Fuzzy Sample Space.- Fuzzy Confidence Regions.- Fuzzy Extensions of Confidence Intervals: Estimation for µ, s 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.