Étude de l'estimateur du maximum de vraisemblance dans le cas d'un processus autorégressif: convergence, normalité asymptotique, vitesse de convergence. (Asymptotic behaviour of maximum likelihood estimator in an autoregressive process: consistency, as

Publication date: 1989

Published in: Annales de l'Institut Henri Poincaré. Probabilités et Statistiques (Search for Journal in Brave)

Full work available at URL: http://www.numdam.org/item?id=AIHPB_1989__25_4_383_0

zbMATH Keywords

convergence in distribution maximum likelihood estimator autoregressive process speed of convergence unique invariant distribution limit law

Mathematics Subject Classification ID

Central limit and other weak theorems (60F05) Point estimation (62F10) Sums of independent random variables; random walks (60G50) Discrete-time Markov processes on general state spaces (60J05)

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Central limit theorems for iterated random Lipschitz mappings. ⋮ A uniform Berry-Esseen theorem on \(M\)-estimators for geometrically ergodic Markov chains ⋮ Robustness of iterated function systems of Lipschitz maps ⋮ The spectral method and the central limit theorem for general Markov chains ⋮ Edgeworth expansion for \(M\)-estimators of \(V\)-geometrically ergodic Markov chains ⋮ The Nagaev-Guivarc’h method via the Keller-Liverani theorem ⋮ Rate of convergence in the central limit theorem for strongly ergodic Markov chains ⋮ The spectral method and the central limit theorem for general Markov chains

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