Springer Fuzzy Statistical Decision-Making: Theory and Applications: 343 (Studies in Fuzziness and Soft Computing, 343)

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. Review “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)  From the Back Cover 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. About the Author 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.