The Annals of Statistics

Parametric rates of nonparametric estimators and predictors for continuous time processes

Denis Bosq

Full-text: Open access

Abstract

We show that local irregularity of observed sample paths provides additional information which allows nonparametric estimators and predictors for continuous time processes to reach parametric rates in mean square as well as in a.s. uniform convergence. For example, we prove that under suitable conditions the kernel density estimator $f_T$ associated with the observed sample path $(X_t, 0 \leq t \leq T)$ satisfies $$\sup_{x \epsilon \mathbb{R}}|f_T(x) - f(x)| = o(\ln_k T(\frac{\ln T}{T})^{1/2}) \quad \text{a.s.}, k \geq 1$$ where f denotes the unknown marginal density of the stationary process and where $\ln_k$ denotes the kth iterated logarithm.

The proof uses a special Borel-Cantelli lemma for continuous time processes together with a sharp large deviation inequality. Furthermore the parametric rate obtained in (1) is preserved by using a suitable sampling scheme.

Article information

Source
Ann. Statist., Volume 25, Number 3 (1997), 982-1000.

Dates
First available in Project Euclid: 20 November 2003

Permanent link to this document
https://projecteuclid.org/euclid.aos/1069362734

Digital Object Identifier
doi:10.1214/aos/1069362734

Mathematical Reviews number (MathSciNet)
MR1447737

Zentralblatt MATH identifier
0885.62041

Subjects
Primary: 62G05: Estimation

Keywords
Nonparametric estimation and prediction parametric rate regression density large deviation inequality

Citation

Bosq, Denis. Parametric rates of nonparametric estimators and predictors for continuous time processes. Ann. Statist. 25 (1997), no. 3, 982--1000. doi:10.1214/aos/1069362734. https://projecteuclid.org/euclid.aos/1069362734


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