Parameter Estimation in Manneville-Pomeau Processes

dc.creatorOlbermann, B. P.
dc.creatorLopes, Silvia R. C.
dc.creatorLopes, Artur O.
dc.date2007-07-11
dc.date.accessioned2026-07-07T08:15:06Z
dc.date.available2026-07-07T08:15:06Z
dc.descriptionIn this work we study a class of stochastic processes $\{X_t\}_{t\in\N}$, where $X_t = (ϕ\circ T_s^t)(X_0)$ is obtained from the iterations of the transformation T_s, invariant for an ergodic probability μ_s on [0,1] and a continuous by part function $ϕ:[0,1] \to \R$. We consider here $T_s:[0,1]\to [0,1]$ the Manneville-Pomeau transformation. The autocorrelation function of the resulting process decays hyperbolically (or polynomially) and we obtain efficient methods to estimate the parameter s from a finite time series. As a consequence we also estimate the rate of convergence of the autocorrelation decay of these processes. We compare different estimation methods based on the periodogram function, on the smoothed periodogram function, on the variance of the partial sum and on the wavelet theory.
dc.identifierhttps://arxiv.org/abs/0707.1600
dc.identifierhttp://arxiv.org/abs/0707.1600
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/133350
dc.subjectStatistics Theory
dc.subjectDynamical Systems
dc.subject62M10; 62M05; 37A25; 37A30
dc.titleParameter Estimation in Manneville-Pomeau Processes
dc.typetext

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