The subject of this paper is autoregressive (AR) modeling of a stationary, Gaussian discrete time process, based on a finite sequence of observations. The process is assumed to admit an AR(∞) representation with exponentially decaying coefficients. We adopt the nonparametric minimax framework and study how well the process can be approximated by a finite-order AR model. A lower bound on the accuracy of AR approximations is derived, and a nonasymptotic upper bound on the accuracy of the regularized least squares estimator is established. It is shown that with a "proper" choice of the model order, this estimator is minimax optimal in order. These considerations lead also to a nonasymptotic upper bound on the mean squared error of the associated one-step predictor. A numerical study compares the common model selection procedures to the minimax optimal order choice.

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Alexander Goldenshluger and Assaf Zeevi
Journal Article
Publication Date
Annals of Statistics

Full Citation

Goldenshluger, Alexander and Assaf Zeevi
. “Nonasymptotic bounds for autoregressive time series modeling.”
Annals of Statistics
, (April 01, 2001):