Wavelet based detection of scaling behaviour in noisy experimental data

Wavelet based detection of scaling behaviour in noisy experimental data

The detection of power-laws in real data is a demanding task for several reasons. The two, more frequently met, being: (i) real data possess noise which affects significantly the power-law tails and (ii) there is no solid tool for the discrimination between a power-law, valid in a specific range of...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:arXiv.org 2019-04
Hauptverfasser: Contoyiannis, Yiannis F, Potirakis, Stelios, Diakonos, Fotios K
Format: Artikel
Sprache:eng
Schlagworte:
Physics - Adaptation and Self-Organizing Systems
Physics - Data Analysis, Statistics and Probability
Power law
Wavelet analysis
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
container_end_page
container_issue
container_start_page
container_title arXiv.org
container_volume
creator Contoyiannis, Yiannis F
Potirakis, Stelios
Diakonos, Fotios K
description The detection of power-laws in real data is a demanding task for several reasons. The two, more frequently met, being: (i) real data possess noise which affects significantly the power-law tails and (ii) there is no solid tool for the discrimination between a power-law, valid in a specific range of scales, from other functional forms like log-normal or stretched exponential distributions. In the present report we demonstrate, employing simulated and real data, that using wavelets it is possible to overcome both of the above mentioned difficulties and achieve a secure detection of a power-law and an accurate estimation of the associated exponent.
doi_str_mv 10.48550/arxiv.1905.01153
format Article
fullrecord <record><control><sourceid>proquest_arxiv</sourceid><recordid>TN_cdi_arxiv_primary_1905_01153</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2220544283</sourcerecordid><originalsourceid>FETCH-LOGICAL-a523-de86865c141e7d3726f9233868145aef617585f7e5056aa27f4ec1799e48741a3</originalsourceid><addsrcrecordid>eNotj01Lw0AURQdBsNT-AFcOuE6crzczWUrRKhTcFFyG1-RFp8SkZqah_ffG1tWFy-VyDmN3UuTGA4hHHI5hzGUhIBdSgr5iM6W1zLxR6oYtYtwJIZR1CkDP2OoDR2op8S1GqnlNiaoU-o73DY8VtqH75Fv6wjH0h4GHjnd9iCdOxz0N4Zu6hC2vMeEtu26wjbT4zznbvDxvlq_Z-n31tnxaZwhKZzV56y1U0khytXbKNsUEN5XSAFJjpQMPjSMQYBGVawxV0hUFGe-MRD1n95fbs2W5nxhwOJV_tuXZdlo8XBb7of85UEzlbiLvJqZSKSXAGOW1_gWAUlaD</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2220544283</pqid></control><display><type>article</type><title>Wavelet based detection of scaling behaviour in noisy experimental data</title><source>arXiv.org</source><source>Free E- Journals</source><creator>Contoyiannis, Yiannis F ; Potirakis, Stelios ; Diakonos, Fotios K</creator><creatorcontrib>Contoyiannis, Yiannis F ; Potirakis, Stelios ; Diakonos, Fotios K</creatorcontrib><description>The detection of power-laws in real data is a demanding task for several reasons. The two, more frequently met, being: (i) real data possess noise which affects significantly the power-law tails and (ii) there is no solid tool for the discrimination between a power-law, valid in a specific range of scales, from other functional forms like log-normal or stretched exponential distributions. In the present report we demonstrate, employing simulated and real data, that using wavelets it is possible to overcome both of the above mentioned difficulties and achieve a secure detection of a power-law and an accurate estimation of the associated exponent.</description><identifier>EISSN: 2331-8422</identifier><identifier>DOI: 10.48550/arxiv.1905.01153</identifier><language>eng</language><publisher>Ithaca: Cornell University Library, arXiv.org</publisher><subject>Physics - Adaptation and Self-Organizing Systems ; Physics - Data Analysis, Statistics and Probability ; Power law ; Wavelet analysis</subject><ispartof>arXiv.org, 2019-04</ispartof><rights>2019. This work is published under http://arxiv.org/licenses/nonexclusive-distrib/1.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,780,784,885,27925</link.rule.ids><backlink>$$Uhttps://doi.org/10.1103/PhysRevE.101.052104$$DView published paper (Access to full text may be restricted)$$Hfree_for_read</backlink><backlink>$$Uhttps://doi.org/10.48550/arXiv.1905.01153$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Contoyiannis, Yiannis F</creatorcontrib><creatorcontrib>Potirakis, Stelios</creatorcontrib><creatorcontrib>Diakonos, Fotios K</creatorcontrib><title>Wavelet based detection of scaling behaviour in noisy experimental data</title><title>arXiv.org</title><description>The detection of power-laws in real data is a demanding task for several reasons. The two, more frequently met, being: (i) real data possess noise which affects significantly the power-law tails and (ii) there is no solid tool for the discrimination between a power-law, valid in a specific range of scales, from other functional forms like log-normal or stretched exponential distributions. In the present report we demonstrate, employing simulated and real data, that using wavelets it is possible to overcome both of the above mentioned difficulties and achieve a secure detection of a power-law and an accurate estimation of the associated exponent.</description><subject>Physics - Adaptation and Self-Organizing Systems</subject><subject>Physics - Data Analysis, Statistics and Probability</subject><subject>Power law</subject><subject>Wavelet analysis</subject><issn>2331-8422</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GOX</sourceid><recordid>eNotj01Lw0AURQdBsNT-AFcOuE6crzczWUrRKhTcFFyG1-RFp8SkZqah_ffG1tWFy-VyDmN3UuTGA4hHHI5hzGUhIBdSgr5iM6W1zLxR6oYtYtwJIZR1CkDP2OoDR2op8S1GqnlNiaoU-o73DY8VtqH75Fv6wjH0h4GHjnd9iCdOxz0N4Zu6hC2vMeEtu26wjbT4zznbvDxvlq_Z-n31tnxaZwhKZzV56y1U0khytXbKNsUEN5XSAFJjpQMPjSMQYBGVawxV0hUFGe-MRD1n95fbs2W5nxhwOJV_tuXZdlo8XBb7of85UEzlbiLvJqZSKSXAGOW1_gWAUlaD</recordid><startdate>20190427</startdate><enddate>20190427</enddate><creator>Contoyiannis, Yiannis F</creator><creator>Potirakis, Stelios</creator><creator>Diakonos, Fotios K</creator><general>Cornell University Library, arXiv.org</general><scope>8FE</scope><scope>8FG</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L6V</scope><scope>M7S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>ALA</scope><scope>GOX</scope></search><sort><creationdate>20190427</creationdate><title>Wavelet based detection of scaling behaviour in noisy experimental data</title><author>Contoyiannis, Yiannis F ; Potirakis, Stelios ; Diakonos, Fotios K</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a523-de86865c141e7d3726f9233868145aef617585f7e5056aa27f4ec1799e48741a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Physics - Adaptation and Self-Organizing Systems</topic><topic>Physics - Data Analysis, Statistics and Probability</topic><topic>Power law</topic><topic>Wavelet analysis</topic><toplevel>online_resources</toplevel><creatorcontrib>Contoyiannis, Yiannis F</creatorcontrib><creatorcontrib>Potirakis, Stelios</creatorcontrib><creatorcontrib>Diakonos, Fotios K</creatorcontrib><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Engineering Collection</collection><collection>Engineering Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>arXiv Nonlinear Science</collection><collection>arXiv.org</collection><jtitle>arXiv.org</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Contoyiannis, Yiannis F</au><au>Potirakis, Stelios</au><au>Diakonos, Fotios K</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Wavelet based detection of scaling behaviour in noisy experimental data</atitle><jtitle>arXiv.org</jtitle><date>2019-04-27</date><risdate>2019</risdate><eissn>2331-8422</eissn><abstract>The detection of power-laws in real data is a demanding task for several reasons. The two, more frequently met, being: (i) real data possess noise which affects significantly the power-law tails and (ii) there is no solid tool for the discrimination between a power-law, valid in a specific range of scales, from other functional forms like log-normal or stretched exponential distributions. In the present report we demonstrate, employing simulated and real data, that using wavelets it is possible to overcome both of the above mentioned difficulties and achieve a secure detection of a power-law and an accurate estimation of the associated exponent.</abstract><cop>Ithaca</cop><pub>Cornell University Library, arXiv.org</pub><doi>10.48550/arxiv.1905.01153</doi><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier EISSN: 2331-8422
ispartof arXiv.org, 2019-04
issn 2331-8422
language eng
recordid cdi_arxiv_primary_1905_01153
source arXiv.org; Free E- Journals
subjects Physics - Adaptation and Self-Organizing Systems
Physics - Data Analysis, Statistics and Probability
Power law
Wavelet analysis
title Wavelet based detection of scaling behaviour in noisy experimental data
url https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-06T13%3A55%3A20IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_arxiv&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Wavelet%20based%20detection%20of%20scaling%20behaviour%20in%20noisy%20experimental%20data&rft.jtitle=arXiv.org&rft.au=Contoyiannis,%20Yiannis%20F&rft.date=2019-04-27&rft.eissn=2331-8422&rft_id=info:doi/10.48550/arxiv.1905.01153&rft_dat=%3Cproquest_arxiv%3E2220544283%3C/proquest_arxiv%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2220544283&rft_id=info:pmid/&rfr_iscdi=true