Open Access
February 2009 Adaptive estimation of linear functionals in the convolution model and applications
C. Butucea, F. Comte
Bernoulli 15(1): 69-98 (February 2009). DOI: 10.3150/08-BEJ146

Abstract

We consider the model $Z_i=X_i+\varepsilon_i$, for i.i.d. $X_i$’s and $\varepsilon_i$’s and independent sequences $(X_i)_{i∈ℕ}$ and $(\varepsilon_i)_{i∈ℕ}$. The density $f_\varepsilon$ of $\varepsilon_1$ is assumed to be known, whereas the one of $X_1$, denoted by $g$, is unknown. Our aim is to estimate linear functionals of $g, 〈ψ, g〉$ for a known function $ψ$. We propose a general estimator of $〈ψ, g〉$ and study the rate of convergence of its quadratic risk as a function of the smoothness of $g, f_\varepsilon$ and $ψ$. Different contexts with dependent data, such as stochastic volatility and AutoRegressive Conditionally Heteroskedastic models, are also considered. An estimator which is adaptive to the smoothness of unknown $g$ is then proposed, following a method studied by Laurent et al. (Preprint (2006)) in the Gaussian white noise model. We give upper bounds and asymptotic lower bounds of the quadratic risk of this estimator. The results are applied to adaptive pointwise deconvolution, in which context losses in the adaptive rates are shown to be optimal in the minimax sense. They are also applied in the context of the stochastic volatility model.

Citation

Download Citation

C. Butucea. F. Comte. "Adaptive estimation of linear functionals in the convolution model and applications." Bernoulli 15 (1) 69 - 98, February 2009. https://doi.org/10.3150/08-BEJ146

Information

Published: February 2009
First available in Project Euclid: 3 February 2009

zbMATH: 1200.62022
MathSciNet: MR2546799
Digital Object Identifier: 10.3150/08-BEJ146

Keywords: Adaptive density estimation , ARCH models , Deconvolution , linear functionals , Model selection , Penalized contrast , stochastic volatility model

Rights: Copyright © 2009 Bernoulli Society for Mathematical Statistics and Probability

Vol.15 • No. 1 • February 2009
Back to Top