Large and moderate deviations for infinite-dimensional autoregressive processes.

DOI10.1016/S0047-259X(03)00053-8zbMath1043.60021OpenAlexW2037953659MaRDI QIDQ1426344

André Mas, Ludovic Menneteau

Publication date: 14 March 2004

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0047-259x(03)00053-8

zbMATH Keywords

autoregressive Hilbertian processesdeviation principles

Mathematics Subject Classification ID

Factor analysis and principal components; correspondence analysis (62H25) Inference from stochastic processes (62M99) Large deviations (60F10) Limit theorems for vector-valued random variables (infinite-dimensional case) (60B12)

Related Items

Moderate deviation principle for autoregressive processes, Consistency of the plug-in functional predictor of the Ornstein-Uhlenbeck process in Hilbert and Banach spaces, The ARHD model, Large deviations for posterior distributions on the parameter of a multivariate \(\mathrm{AR}(p)\) process, Explicit bivariate rate functions for large deviations in AR(1) and MA(1) processes with Gaussian innovations, Asymptotic properties of a component-wise ARH(1) plug-in predictor, Moderate deviations for stationary sequences of Hilbert-valued bounded random variables, The Discounted Large Deviation Principle for Autoregressive Processes, Asymptotic normality of autoregressive processes, Some laws of the iterated logarithm in Hilbertian autoregressive models, Moderate Deviation Principles for Empirical Covariance in the Neighbourhood of the Unit Root, Large deviations for Bayesian estimators in first-order autoregressive processes, The asymptotic behaviors for least square estimation of multi-casting autoregressive processes, Weak convergence in the functional autoregressive model, Moderate deviations in a class of stable but nearly unstable processes, The Discounted Berry-Esséen Analogue for Autoregressive Processes

Uses Software

• fda (R)

Cites Work

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