PARAMETER TUNING IN POINTWISE ADAPTATION USING A PROPAGATION APPROACH

• 成果类型: Article
• 署名作者: Spokoiny, Vladimir; Vial, Celine
• 署名单位: Leibniz Association; Weierstrass Institute for Applied Analysis & Stochastics; Humboldt University of Berlin; Ecole Nationale de la Statistique et de l'Analyse de l'Information (ENSAI)
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/08-AOS607
• 发表日期: 2009
• 页码: 2783-2807
• 关键词: minimum contrast estimators; Adaptive estimation; nonparametric-estimation; linear functionals; hilbert scales; white-noise; CONVERGENCE; rates; bounds

摘要

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, Lip 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.