Extreme Value and Related Models with Applications in Engineering and Science / Edition 1

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This book provides readers with an elementary and comprehensive discussion on extreme value and related models. By using a large number of practical data from different science and engineering disciplines, it illustrates the practical importance and usefulness of extreme value modeling. Unusual, provocative, and concrete examples are derived from areas such as ocean engineering, structural engineering, hydraulics, meteorology, materials science, fatigue studies, electrical strength of materials, highway traffic analysis, corrosion science, environmetrics, climatology, among others.

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Editorial Reviews

From the Publisher
“This text provides an extensive coverage of the distribution theory of extreme values…the text is well presented with clearly displayed equations and diagrams…a particularly useful reference book…” (International Statistical Institute, Vol 25 (2) August 2005)
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Product Details

Meet the Author

ENRIQUE CASTILLO, PhD, is a Professor of Applied Mathematics at the University of Cantabria in Santander, Spain. He is a mathematician and a civil engineer and member of the Spanish Royal Academy of Engineering.

ALI S. HADI, PhD, is a Professor of Mathematical, Statistical, and Computational Sciences at the American University in Cairo, Egypt. He is a Stephen H. Weiss Presidential Fellow and Professor Emeritus at Cornell University.

N. BALAKRISHNAN, PhD, is a Professor in the Department of Mathematics and Statistics at McMaster University in Ontario, Canada. He is a Fellow of the American Statistical Association and currently the Editor-in-Chief of Communications in Statistics and Wiley's Encyclopedia of Statistical Sciences, Second Edition.

JOSÉ M. SARABIA, PhD, is a Professor of Statistics in the Department of Economics at the University of Cantabria, Spain. He is a member of the American Statistical Association and is Associate Editor of the journal Test.

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Table of Contents



1. Introduction and Motivation.


2. Discrete Probabilistic Models.

3. Continuous Probabilistic Models.


4. Model Estimation.

5. Model Selection and Validation.


6. Order Statistics.

7. Point Processes and Exact Models.


8. Limit Distributions of Order Statistics.

9. Limit Distributions of Exceedances.

10. Multivariate Extremes.

Appendix: Statistical Tables.



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