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 Long-range dependence in coastal framework geology: Asymmetries and implications for barrier island resiliency

2018 ◽  
Author(s):  
Phillipe Wernette ◽  
Chris Houser ◽  
Bradley Weymer ◽  
Mark Everett ◽  
Michael Bishop ◽  
...  
Keyword(s):  
Long Range ◽  
Coastal Management ◽  
Moving Average ◽  
Barrier Island ◽  
Long Range Dependence ◽  
The North ◽  
Arfima Models ◽  
Societal Pressure ◽  
Dune Morphology

Abstract. Barrier island transgression is influenced by the alongshore variation in beach and dune morphology, which determines the amount of sediment moved landward through washover. While several studies have demonstrated how variations in dune morphology affect island response to storms, the reasons for that variation and the implications for island management remain unclear. This paper builds on previous research by demonstrating that the framework geology can influence beach and dune morphology asymmetrically alongshore. The influence of relict paleo-channels on beach and dune morphology on Padre Island National Seashore, Texas was quantified by isolating the long-range dependence (LRD) parameter in autoregressive fractionally-integrated moving average (ARFIMA) models. ARFIMA models were fit across all scales and a moving window approach was used to examine how LRD varied with computational scale and location along the island. The resulting LRD matrices were plotted by latitude to place the results in context of previously identified variations in the framework geology. Results indicate that the LRD is not constant alongshore for all surface morphometrics. Many flares in the LRD plots correlate to relict infilled paleo-channels in the framework geology, indicating that the framework geology has a significant influence on the morphology of PAIS. Barrier island surface morphology LRD is strongest at large paleo-channels and decreases to the north. The spatial patterns in LRD surface morphometrics and framework geology variations demonstrate that the influence of paleo-channels in the framework geology can be asymmetric where the alongshore sediment transport gradient was unidirectional during island development. The asymmetric influence of framework geology on coastal morphology has long-term implications for coastal management activities because it dictates the long-term behavior of a barrier island. Coastal management projects should first seek to understand how the framework geology influences coastal processes in order to more effectively balance long-term natural variability with short-term societal pressure.

scholarly journals Analysis of Peak Flow Distribution for Bridge Collapse Sites

Water ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 52 ◽  
Author(s):  
Fahmidah U. Ashraf ◽  
Madeleine M. Flint
Keyword(s):  
Peak Flow ◽  
Flow Distribution ◽  
Heavy Tail ◽  
Flood Frequency ◽  
Return Periods ◽  
Peak Flows ◽  
Tail Distribution ◽  
Bridge Collapse ◽  
Flood Magnitude ◽  
Distribution Parameters

Bridge collapse risk can be evaluated more rigorously if the hydrologic characteristics of bridge collapse sites are demystified, particularly for peak flows. In this study, forty-two bridge collapse sites were analyzed to find any trend in the peak flows. Flood frequency and other statistical analyses were used to derive peak flow distribution parameters, identify trends linked to flood magnitude and flood behavior (how extreme), quantify the return periods of peak flows, and compare different approaches of flood frequency in deriving the return periods. The results indicate that most of the bridge collapse sites exhibit heavy tail distribution and flood magnitudes that are well consistent when regressed over the drainage area. A comparison of different flood frequency analyses reveals that there is no single approach that is best generally for the dataset studied. These results indicate a commonality in flood behavior (outliers are expected, not random; heavy-tail property) for the collapse dataset studied and provides some basis for extending the findings obtained for the 42 collapsed bridges to other sites to assess the risk of future collapses.

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

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 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.

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