- Hardcover: 540 pages
- Publisher: Chapman and Hall/CRC; 1 edition (December 21, 2015)
- Language: English
- ISBN-10: 1498701299
- ISBN-13: 978-1498701297
- Product Dimensions: 1.2 x 6.5 x 9.2 inches
- Shipping Weight: 1.8 pounds (View shipping rates and policies)
- Average Customer Review: 4.0 out of 5 stars See all reviews (1 customer review)
- Amazon Best Sellers Rank: #3,456,799 in Books (See Top 100 in Books)
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Extreme Value Modeling and Risk Analysis: Methods and Applications 1st Edition
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"This book has a broad readership, and it will be useful to graduate students and researchers in the field of statistics, finances, insurance, economics, geosciences, etc.. . This is an impressive and useful book, one which gives an effective account of statistical methods and theory used in extreme value analysis, as applied to problems arising in a variety of fields. It serves as an excellent, contemporary reference text in the area."
~International Statistical Review (2017)
About the Author
Jun Yan is a professor in the Department of Statistics at the University of Connecticut. He was previously an assistant professor at the University of Iowa. He received a Ph.D. in statistics from the University of Wisconsin–Madison. His research interests include spatial extremes, copulas, survival analysis, estimating equations, clustered data analysis, statistical computing, and applications in public health and environment.
Dipak K. Dey is a Board of Trustees Distinguished Professor in the Department of Statistics and associate dean of the College of Liberal Arts and Sciences at the University of Connecticut. He is an elected fellow of the International Society for Bayesian Analysis and American Association for the Advancement of Science, an elected member of the Connecticut Academy of Arts and Sciences and International Statistical Institute, and a fellow of the American Statistical Association and Institute of Mathematical Statistics. Dr. Dey is a co-editor and co-author of several books, including the Chapman & Hall/CRC Bayesian Modeling in Bioinformatics and A First Course in Linear Model Theory. His research interests include Bayesian analysis, bioinformatics, biostatistics, computational statistics, decision theory, environmental statistics, multivariate analysis, optics, reliability and survival analysis, statistical shape analysis, and statistical genetics.
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