Hostname: page-component-78c5997874-ndw9j Total loading time: 0 Render date: 2024-11-08T02:22:37.000Z Has data issue: false hasContentIssue false

Minimax results for estimating integrals of analytic processes

Published online by Cambridge University Press:  15 August 2002

Karim Benhenni
Affiliation:
Jacques Istas
Affiliation:
Get access

Abstract

The problem of predicting integrals of stochastic processes is considered. Linear estimators have been constructed by means of samples at N discrete times for processes having a fixedHölderian regularity s > 0 in quadratic mean. It is known that the rate of convergence of the mean squared error is of order N-(2s+1). In the class of analytic processes H p , p ≥ 1, we show that among all estimators, the linear ones are optimal. Moreover, using optimal coefficient estimators derived through the inversion of the covariance matrix, the corresponding maximal error has lower and upper bounds with exponential rates. Optimal simple nonparametric estimators with optimal sampling designs are constructed in H² and H and have also bounds with exponential rates.

Type
Research Article
Copyright
© EDP Sciences, SMAI, 1998

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)