Hurst exponent estimation of locally self-similar Gaussian processes using sample quantiles
| dc.creator | Coeurjolly, Jean-François | |
| dc.date | 2005-06-15 | |
| dc.date | 2007-02-08 | |
| dc.date.accessioned | 2026-07-07T08:06:59Z | |
| dc.date.available | 2026-07-07T08:06:59Z | |
| dc.description | This paper is devoted to the introduction of a new class of consistent estimators of the fractal dimension of locally self-similar Gaussian processes. These estimators are based on convex combinations of sample quantiles of discrete variations of a sample path over a discrete grid of the interval $[0,1]$. We derive the almost sure convergence and the asymptotic normality for these estimators. The key-ingredient is a Bahadur representation for sample quantiles of non-linear functions of Gaussians sequences with correlation function decreasing as $k^{-α}L(k)$ for some $α>0$ and some slowly varying function $L(\cdot)$. | |
| dc.description | 44 pages, février 2007 | |
| dc.identifier | https://arxiv.org/abs/math/0506290 | |
| dc.identifier | http://arxiv.org/abs/math/0506290 | |
| dc.identifier.uri | http://salesiana.dossiersoluciones.com/handle/123456789/130795 | |
| dc.subject | Statistics Theory | |
| dc.subject | 60G18, 62G30 | |
| dc.title | Hurst exponent estimation of locally self-similar Gaussian processes using sample quantiles | |
| dc.type | text |