Computing the best linear predictor in a Hilbert space. Applications to general ARMAH processes

DOI10.1016/J.JMVA.2013.11.013zbMath1278.62153OpenAlexW2086077409MaRDI QIDQ392108

Denis Bosq

Publication date: 13 January 2014

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

Full work available at URL: https://doi.org/10.1016/j.jmva.2013.11.013

zbMATH Keywords

predictionHilbert spaceslinear processesmeasurable linear transformationsfunctional filterslarge dimensions

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Bayesian inference (62F15) Applications of functional analysis in probability theory and statistics (46N30)

Related Items (8)

Seasonal functional autoregressive models ⋮ Consistency of the regression estimator with functional data under long memory conditions ⋮ Functional data analysis by matrix completion ⋮ An innovations algorithm for the prediction of functional linear processes ⋮ Approximating the critical values of Cramér-von Mises tests in general parametric conditional specifications ⋮ A central limit theorem for the linear process generated by associated random variables in a Hilbert space ⋮ Best linear predictor of a \(C_{[0, 1}\)-valued functional autoregressive process] ⋮ Functional nonparametric estimation of conditional extreme quantiles

Cites Work

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• Curve forecasting by functional autoregression
• Kalman filtering from POP-based diagonalization of ARH(1)
• Linear estimators and measurable linear transformations on a Hilbert space
• Inference and Prediction in Large Dimensions
• The Generalized Dynamic Factor Model

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