The modeling of extreme values is important to scientists in such  elds as hydrology, civil engineering, environmental science, oceanography and  nance. Stuart Coles’s book on the modeling of extreme values provides an introductory text on the topic. It is a modeling-oriented text with an emphasis on different types of data and analytical approaches. The book is laid out in nine chapters. Following introductory material and discussion of necessary theoretical background are chapters on approaches to extreme values that focus on the different types of data that might be used in an extreme value analysis. These include models for block maximums, threshold models, models for data from stationary and nonstationary processes, and approaches based on point processes. A chapter covers analysis of multivariate extremes, and the  nal chapter brie y covers such topics as Bayesian inference, Markov chains, and spatial extremes. Although this is not a data-driven text, it does contain numerous examples and analyses. These examples are used to illustrate the methodology; I would have preferred to see more motivation and interpretation of the results of the analyses. Datasets and S-PLUS programs for the analyses in the text are available at a website. These are easy to use for those slightly familiar with S-PLUS. The appendix describes the programs and illustrates how to access data and use the programs. It also gives links to sites that provide other software. The text does not include problem sets; these would have been useful, especially if the text is to be used in coursework. The text by Reiss and Thomas (2001) contains more thoroughly analyzed datasets, although it is twice the length and not as streamlined as the text under review. The book is meant for individuals with moderate statistical background. Those with coursework in maximum likelihood methods should have no dif culty reading and comprehending the text. Overall, this is a good text for someone getting started in extreme value methods.

Statistical Quality Control

Analytic Hierarchy Process (AHP) is a broadly applied multi-criteria decision-making method to determine the weights of criteria and priorities of alternatives in a structured manner based on pairwise comparison. As subjective judgments during comparison might be imprecise, fuzzy sets have been combined with AHP. This is referred to as fuzzy AHP or FAHP. An increasing amount of papers are published which describe different ways to derive the weights/priorities from a fuzzy comparison matrix, but seldomly set out the relative benefits of each approach so that the choice of the approach seems arbitrary. A review of various fuzzy AHP techniques is required to guide both academic and industrial experts to choose suitable techniques for a specific practical context. This paper reviews the literature published since 2008 where fuzzy AHP is applied to decision-making problems in industry, particularly the various selection problems. The techniques are categorised by the four aspects of developing a fuzzy AHP model: (i) representation of the relative importance for pairwise comparison, (ii) aggregation of fuzzy sets for group decisions and weights/priorities, (iii) defuzzification of a fuzzy set to a crisp value for final comparison, and (iv) consistency measurement of the judgements. These techniques are discussed in terms of their underlying principles, origins, strengths and weakness. Summary tables and specification charts are provided to guide the selection of suitable techniques. Tips for building a fuzzy AHP model are also included and six open questions are posed for future work.

A review of fuzzy AHP methods for decision-making with subjective judgements

Ranking fuzzy numbers by distance minimization

Similarity measures of intuitionistic fuzzy sets based on L p metric

http://www.appliedlogictudelft.nl/wp-content/uploads/2014/02/U-Rivieccio_Neutrosophic-Logics-Prospect-and-Problems_Applied-Logic-TU-Delft.pdf

Neutrosophic logics: Prospects and problems

Trapezoidal approximations of fuzzy numbers

Trapezoidal approximations of fuzzy numbers---revisited

Discussion: Some notes on (Atanassov's) intuitionistic fuzzy sets

The notion of probability measure of intuitionistic fuzzy events is investigated and basic properties of that concept are examined.

Probability of Intuitionistic Fuzzy Events

The problem of the trapezoidal approximation of fuzzy numbers is discussed. A set of criteria for approximation operators is formulated. A new nearest trapezoidal approximation operator preserving expected interval is suggested.

Edyta Mrówka

Papers

Trapezoidal approximations of fuzzy numbers

Trapezoidal approximations of fuzzy numbers---revisited

Discussion: Some notes on (Atanassov's) intuitionistic fuzzy sets

Probability of Intuitionistic Fuzzy Events

Trapezoidal approximations of fuzzy numbers