On shape-preserving probabilistic wavelet approximators
作者:
Lubomir Dechevsky,
Spiridon Penev,
期刊:
Stochastic Analysis and Applications
(Taylor Available online 1997)
卷期:
Volume 15,
issue 2
页码: 187-215
ISSN:0736-2994
年代: 1997
DOI:10.1080/07362999708809471
出版商: Marcel Dekker, Inc.
数据来源: Taylor
摘要:
We introduce a general class of shape-preserving wavelet approximating operators (approximators) which transform cumulative distribution functions and densities into functions of the same type. Our operators can be considered as a generalization of the operators introduced by Anastassiou and Yu [1]. Further, we extend the consideration by studying the approximation properties for the whole variety ofLp:-norms, 0<p≤∞. In [1] the casep=∞ is discussed. Using the properties of integral moduli of smoothness, we obtain various approximation rates under no (or minimal) additional assumptions on the functions to be approximated. These assumptions are in terms of the function or its Riesz potential belonging to certain homogeneous Besov, Triebel-Lizorkin, Sobolev spaces, the paceBVpof functions with bounded Wiener-Youngp-variation, etc
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