Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
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Authors: Ludger Evers, Tim Heaton
Title: Locally Adaptive Tree-Based Thresholding Using the treethresh Package in R
Abstract: This paper introduces the treethresh package offering accurate estimation, via thresholding, of potentially sparse heterogeneous signals and the denoising of images using wavelets. It gives considerably improved performance over other estimation methods if the underlying signal or image is not homogeneous throughout but instead has distinct regions with differing sparsity or strength characteristics. It aims to identify these different regions and perform separate estimation in each accordingly. The base algorithm offers code which can be applied directly to any one-dimensional potentially sparse sequence observed subject to noise. Also included are functions which allow two-dimensional images to be denoised following transformation to the wavelet domain. In addition to reconstructing the underlying signal or image, the package provides information on the believed partitioning of the signal or image into its differing regions.

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Paper: Locally Adaptive Tree-Based Thresholding Using the treethresh Package in R     Download PDF (Downloads: 466)
treethresh_0.1-11.tar.gz: R source package Download (Downloads: 98; 1MB)
v78c02.R: R replication code Download (Downloads: 127; 8KB)
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simulation_cache.RData: Replication materials Download (Downloads: 130; 14KB)

DOI: 10.18637/jss.v078.c02

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.