The Annals of Statistics

Parameter tuning in pointwise adaptation using a propagation approach

Vladimir Spokoiny and Céline Vial

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This paper discusses the problem of adaptive estimation of a univariate object like the value of a regression function at a given point or a linear functional in a linear inverse problem. We consider an adaptive procedure originated from Lepski [Theory Probab. Appl. 35 (1990) 454–466.] that selects in a data-driven way one estimate out of a given class of estimates ordered by their variability. A serious problem with using this and similar procedures is the choice of some tuning parameters like thresholds. Numerical results show that the theoretically recommended proposals appear to be too conservative and lead to a strong oversmoothing effect. A careful choice of the parameters of the procedure is extremely important for getting the reasonable quality of estimation. The main contribution of this paper is the new approach for choosing the parameters of the procedure by providing the prescribed behavior of the resulting estimate in the simple parametric situation. We establish a non-asymptotical “oracle” bound, which shows that the estimation risk is, up to a logarithmic multiplier, equal to the risk of the “oracle” estimate that is optimally selected from the given family. A numerical study demonstrates a good performance of the resulting procedure in a number of simulated examples.

Article information

Ann. Statist., Volume 37, Number 5B (2009), 2783-2807.

First available in Project Euclid: 17 July 2009

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 62G05: Estimation 62G05: Estimation
Secondary: 62G10: Hypothesis testing 62G10: Hypothesis testing

Linear functional inverse problem propagation oracle


Spokoiny, Vladimir; Vial, Céline. Parameter tuning in pointwise adaptation using a propagation approach. Ann. Statist. 37 (2009), no. 5B, 2783--2807. doi:10.1214/08-AOS607.

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