文章基本信息

标题：Multichannel boxcar deconvolution with growing number of channels
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作者：Marianna Pensky ; Theofanis Sapatinas
期刊名称：Electronic Journal of Statistics
印刷版ISSN：1935-7524
出版年度：2011
卷号：5
页码：53-82
DOI：10.1214/11-EJS597
语种：English
出版社：Institute of Mathematical Statistics
摘要：We consider the problem of estimating the unknown response function in the multichannel deconvolution model with a boxcar-like kernel which is of particular interest in signal processing. It is known that, when the number of channels is finite, the precision of reconstruction of the response function increases as the number of channels M grow (even when the total number of observations n for all channels M remains constant) and this requires that the parameter of the channels form a Badly Approximable M-tuple.
关键词：Adaptivity;badly approximable tuples;Besov spaces;Diophantine approximation;functional deconvolution;Fourier anal ysis;Meyer wavelets;nonparametric estimation;wavelet analysis.