Fuzzy Statistical Decision-Making

Kahraman C. , Kabak Ö.

Fuzzy Statistical Decision-Making: Theory and Applications, vol.343, pp.1-12, 2016 (Refereed Journals of Other Institutions) identifier identifier

  • Publication Type: Article / Article
  • Volume: 343
  • Publication Date: 2016
  • Doi Number: 10.1007/978-3-319-39014-7_1
  • Title of Journal : Fuzzy Statistical Decision-Making: Theory and Applications
  • Page Numbers: pp.1-12
  • Keywords: Fuzzy statistics, Statistical decision-making, Fuzzy event, Classification, C-MEANS ALGORITHM, SET-THEORY


The classification of decision-making methods can be based on the types of the data in hand. If the data are given as a decision matrix with discrete values, you can use multiple attribute decision-making. If the data are given as unit cost or profit values together with budget or capacity constraints and if you have more than one objective, then you can use multiple objective decision-making in a continuous space. If the data are given as the parameters of certain probability distributions, then you can use statistical decision-making, generally through hypothesis tests. If the data are not exactly known, the fuzzy sets based approaches are incorporated into these decision-making methods. Fuzzy statistical decision-making is one of the most often used methods when insufficient statistical data exist in hand. Fuzzy hypothesis tests, fuzzy variance analysis, and fuzzy design of experiments are the examples of fuzzy statistical decision-making techniques. In this chapter, we survey the literature of fuzzy statistics and fuzzy statistical decision-making and present the results by graphical illustrations.