Long-range dependence and heavy-tail modeling for teletraffic data

2002 ◽  
Vol 19 (3) ◽  
pp. 14-27 ◽  
Author(s):  
O. Cappe ◽  
E. Moulines ◽  
J.-C. Pesquet ◽  
A.P. Petropulu ◽  
Xueshi Yang
Keyword(s):  
Long Range ◽  
Heavy Tail ◽  
Long Range Dependence

scholarly journals Revisiting an old friend: on the observability of the relation between long range dependence and heavy tail

2009 ◽  
Vol 43 (3-4) ◽  
pp. 147-165 ◽  
Author(s):  
Patrice Abry ◽  
Pierre Borgnat ◽  
Fabio Ricciato ◽  
Antoine Scherrer ◽  
Darryl Veitch
Keyword(s):  
Long Range ◽  
Heavy Tail ◽  
Long Range Dependence

scholarly journals Systematic inference of the long-range dependence and heavy-tail distribution parameters of ARFIMA models

2017 ◽  
Vol 473 ◽  
pp. 60-71 ◽  
Author(s):  
Timothy Graves ◽  
Christian L.E. Franzke ◽  
Nicholas W. Watkins ◽  
Robert B. Gramacy ◽  
Elizabeth Tindale
Keyword(s):  
Long Range ◽  
Heavy Tail ◽  
Long Range Dependence ◽  
Arfima Models ◽  
Tail Distribution ◽  
Distribution Parameters

scholarly journals Representations of hermite processes using local time of intersecting stationary stable regenerative sets

2020 ◽  
Vol 57 (4) ◽  
pp. 1234-1251
Author(s):  
Shuyang Bai
Keyword(s):  
Long Range ◽  
Local Time ◽  
Limit Theorems ◽  
Local Times ◽  
Long Range Dependence ◽  
Random Interval ◽  
Stationary Increments ◽  
Self Similar ◽  
Regenerative Sets

AbstractHermite processes are a class of self-similar processes with stationary increments. They often arise in limit theorems under long-range dependence. We derive new representations of Hermite processes with multiple Wiener–Itô integrals, whose integrands involve the local time of intersecting stationary stable regenerative sets. The proof relies on an approximation of regenerative sets and local times based on a scheme of random interval covering.

scholarly journals On nonparametric regression for bivariate circular long-memory time series

2021 ◽  
Author(s):  
Jan Beran ◽  
Britta Steffens ◽  
Sucharita Ghosh
Keyword(s):  
Time Series ◽  
Nonparametric Regression ◽  
Long Range ◽  
Long Memory ◽  
Regression Function ◽  
Confidence Bands ◽  
Long Range Dependence ◽  
Asymptotic Results ◽  
Memory Time ◽  
Circular Time Series

AbstractWe consider nonparametric regression for bivariate circular time series with long-range dependence. Asymptotic results for circular Nadaraya–Watson estimators are derived. Due to long-range dependence, a range of asymptotically optimal bandwidths can be found where the asymptotic rate of convergence does not depend on the bandwidth. The result can be used for obtaining simple confidence bands for the regression function. The method is illustrated by an application to wind direction data.

Long range dependence in stock market returns

2006 ◽  
Vol 16 (18) ◽  
pp. 1331-1338 ◽  
Author(s):  
Christos Christodoulou-Volos ◽  
Fotios M. Siokis
Keyword(s):  
Stock Market ◽  
Long Range ◽  
Long Range Dependence ◽  
Market Returns ◽  
Stock Market Returns
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