Nonasymptotic bounds for autoregressive time series modeling.

DOI10.1214/aos/1009210547zbMath1041.62074OpenAlexW1523507525MaRDI QIDQ1848866

Alexander Goldenshluger, Assaf J. Zeevi

Publication date: 14 November 2002

Published in: The Annals of Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1214/aos/1009210547

zbMATH Keywords

rates of convergenceminimax riskautoregressive approximation

Mathematics Subject Classification ID

Inference from stochastic processes and prediction (62M20) Nonparametric regression and quantile regression (62G08) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Nonparametric estimation (62G05)

Related Items

Convergence of the least-squares method with a polynomial regularizer for the infinite-dimensional autoregression equation, Accumulated prediction errors, information criteria and optimal forecasting for autoregressive time series, Strong consistency of the regularized least-squares estimates of infinite autoregressive models, Maximum likelihood estimators in linear regression models with Ornstein-Uhlenbeck process, Simultaneous confidence bands for sequential autoregressive fitting, On the sample complexity of the linear quadratic regulator, Data-driven model selection for same-realization predictions in autoregressive processes, Autoregressive process modeling via the Lasso procedure, Nonparametric sequential prediction of time series, QML estimators in linear regression models with functional coefficient autoregressive processes, Optimal prediction for linear regression with infinitely many parameters.

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