On recursive estimation for time varying autoregressive processes

dc.creatorMoulines, Eric
dc.creatorPriouret, Pierre
dc.creatorRoueff, François
dc.date2006-03-02
dc.date.accessioned2026-07-07T08:07:37Z
dc.date.available2026-07-07T08:07:37Z
dc.descriptionThis paper focuses on recursive estimation of time varying autoregressive processes in a nonparametric setting. The stability of the model is revisited and uniform results are provided when the time-varying autoregressive parameters belong to appropriate smoothness classes. An adequate normalization for the correction term used in the recursive estimation procedure allows for very mild assumptions on the innovations distributions. The rate of convergence of the pointwise estimates is shown to be minimax in $β$-Lipschitz classes for $0<β\leq1$. For $1<β\leq 2$, this property no longer holds. This can be seen by using an asymptotic expansion of the estimation error. A bias reduction method is then proposed for recovering the minimax rate.
dc.descriptionPublished at http://dx.doi.org/10.1214/009053605000000624 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)
dc.identifierhttps://arxiv.org/abs/math/0603047
dc.identifierhttp://arxiv.org/abs/math/0603047
dc.identifierAnnals of Statistics 2005, Vol. 33, No. 6, 2610-2654
dc.identifierdoi:10.1214/009053605000000624
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/130989
dc.subjectStatistics Theory
dc.subject62M10, 62G08, 60J27 (Primary) 62G20 (Secondary)
dc.titleOn recursive estimation for time varying autoregressive processes
dc.typetext

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