- We will send in 10–14 business days.
/images/books/3393822/9780387759524.jpg)
- Author: Sidney I Resnick
- Publisher: Springer
- ISBN-10: 0387759522
- ISBN-13: 9780387759524
- Format: 16.2 x 23.6 x 1.6 cm, softcover
- Language: English
- SAVE -10% with code: EXTRA
Extreme Values, Regular Variation and Point Processes (e-book) (used book) | bookbook.eu
Reviews
Description
This book presents a readable, efficient account of the fundamental mathematical and stochastic process techniques needed to study the behavior of extreme values of phenomena based on independent and identically distributed random variables and vectors. It presents a coherent treatment of the distributional and sample path fundamental properties of extremes and records. It emphasizes the core primacy of three topics necessary for understanding extremes: the analytical theory of regularly varying functions; the probabilistic theory of point processes and random measures; and the link to asymptotic distribution approximations provided by the theory of weak convergence of probability measures in metric spaces.
EXTRA 10 % discount with code: EXTRA
The promotion ends in 20d.08:33:59
The discount code is valid when purchasing from 10 €. Discounts do not stack.
- Author: Sidney I Resnick
- Publisher: Springer
- ISBN-10: 0387759522
- ISBN-13: 9780387759524
- Format: 16.2 x 23.6 x 1.6 cm, softcover
- Language: English English
This book presents a readable, efficient account of the fundamental mathematical and stochastic process techniques needed to study the behavior of extreme values of phenomena based on independent and identically distributed random variables and vectors. It presents a coherent treatment of the distributional and sample path fundamental properties of extremes and records. It emphasizes the core primacy of three topics necessary for understanding extremes: the analytical theory of regularly varying functions; the probabilistic theory of point processes and random measures; and the link to asymptotic distribution approximations provided by the theory of weak convergence of probability measures in metric spaces.
Reviews