A Smirnov-Bickel-Rosenblatt Theorem for Compactly-Supported Wavelets

作者：Bull Adam D^*

来源：Constructive Approximation, 2013, 37(2): 295-309.

DOI：10.1007/s00365-013-9181-7

• Nonparametric statistics
• Compactly-supported wavelets
• Asymptotic distribution
• Confidence sets
• Supremum norm

In nonparametric statistical problems, we wish to find an estimator of an unknown function f. We can split its error into bias and variance terms; Smirnov, Bickel and Rosenblatt have shown that, for a histogram or kernel estimate, the supremum norm of the variance term is asymptotically distributed as a Gumbel random variable. In the following, we prove a version of this result for estimators using compactly-supported wavelets, a popular tool in nonparametric statistics. Our result relies on an assumption on the nature of the wavelet, which must be verified by provably-good numerical approximations. We verify our assumption for Daubechies wavelets and symlets, with N=6,aEuro broken vertical bar,20 vanishing moments; larger values of N, and other wavelet bases, are easily checked, and we conjecture that our assumption holds also in those cases.

• 出版日期2013-4