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Wavelet-Based estimation of multivariate regression functions in besov spaces *
Author:
Elias Masry a
| Affiliation: | a Department of Electrical and Computer Engineering, University of California, San Diego, California |
DOI:
10.1080/10485250008832809
Publication Frequency:
8 issues per year
Subjects:
Mathematical Economics;
Mathematical Finance;
Medical Statistics;
Statistical Theory & Methods;
Statistics;
Statistics for the Biological Sciences;
Stochastic Models & Processes;
Formats available:
PDF
(English)
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Abstract
Let (Y, X) =
 Yi, Xi be real-valued jointly stationary processes and let ρ be a Borel measurable function on the real line. Let  be a d-dimensional regression function. For regression functions in the Besov space Bs,p,qwe estimate g using orthonormal wavelet bases. Uniform rates of almost sure convergence over compact subsets of Rd are established for strongly mixing processes.
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*This work was supported by the National Science Foundation under grant DMS-97-03876.
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| Keywords: Multivariate regression estimation; wavelet bases; Besov spaces; rates of strong convergence; strongly mixing processes |
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