- We will send in 10–14 business days.
/images/books/11350001/9781601980908.jpg)
- Author: Gennady Samorodnitsky
- Publisher: Now Publishers
- ISBN-10: 1601980906
- ISBN-13: 9781601980908
- Format: 15.6 x 23.4 x 0.6 cm, minkšti viršeliai
- Language: English
- SAVE -10% with code: EXTRA
Reviews
Description
Long Range Dependence is a wide ranging survey of the ideas, models and techniques associated with the notion of long memory. It begins with a historical survey going back to W. Hurst and the Nile river data, and goes on to discuss the various traditional and new points of view on long range dependence. These include connections with non-stationary processes, with ergodic theory, with self-similar processes and with fractionally differenced processes. The survey considers the implications of long memory on stochastic models with heavy tails and light tails, on processes defined as stochastic integrals, single and multiple, on limit theorems and on large deviations. Long Range Dependence will serve as an invaluable reference source for researchers studying long range dependence, for those building long memory models, and for people who are trying to detect the possible presence of long memory in data.
EXTRA 10 % discount with code: EXTRA
The promotion ends in 23d.11:50:24
The discount code is valid when purchasing from 10 €. Discounts do not stack.
- Author: Gennady Samorodnitsky
- Publisher: Now Publishers
- ISBN-10: 1601980906
- ISBN-13: 9781601980908
- Format: 15.6 x 23.4 x 0.6 cm, minkšti viršeliai
- Language: English English
Long Range Dependence is a wide ranging survey of the ideas, models and techniques associated with the notion of long memory. It begins with a historical survey going back to W. Hurst and the Nile river data, and goes on to discuss the various traditional and new points of view on long range dependence. These include connections with non-stationary processes, with ergodic theory, with self-similar processes and with fractionally differenced processes. The survey considers the implications of long memory on stochastic models with heavy tails and light tails, on processes defined as stochastic integrals, single and multiple, on limit theorems and on large deviations. Long Range Dependence will serve as an invaluable reference source for researchers studying long range dependence, for those building long memory models, and for people who are trying to detect the possible presence of long memory in data.
Reviews