Spectral density estimation for random processes with stationary increments
Spectral density analysis plays an important role in studying a stationary random process on a real line. In this paper, we extend this discussion for the random process with stationary increments. We investigate the properties of the method of moments structure function estimation, and propose a no...
Gespeichert in:
| Veröffentlicht in: | Applied stochastic models in business and industry 2024-07, Vol.40 (4), p.960-978 |
|---|---|
| Hauptverfasser: | , , , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 978 |
|---|---|
| container_issue | 4 |
| container_start_page | 960 |
| container_title | Applied stochastic models in business and industry |
| container_volume | 40 |
| creator | Chen, Wei Huang, Chunfeng Zhang, Haimeng Schaffer, Matthew |
| description | Spectral density analysis plays an important role in studying a stationary random process on a real line. In this paper, we extend this discussion for the random process with stationary increments. We investigate the properties of the method of moments structure function estimation, and propose a nonparametric spectral density function estimator. Our numerical results show that the proposed spectral density estimator performs comparable with the parametric counterpart when the underlying process is assumed to be band‐limited. Additionally, this method is applied to analyze US Housing Starts Data, where the hidden periodicities are detected, providing consistent conclusions with previous economic studies. |
| doi_str_mv | 10.1002/asmb.2857 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_3093003971</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>3093003971</sourcerecordid><originalsourceid>FETCH-LOGICAL-c2927-b067c1039e24198ea0b64cf712a624ec0821e51d847d9714f3bb528a3264b6bf3</originalsourceid><addsrcrecordid>eNp1kD9PwzAQxS0EEqUw8A0sMTGkvXOcOBlLxT9RxFCYLdtxRKomKbarKt8ep2Vluht-9-69R8gtwgwB2Fz5Vs9YkYkzMsGM5QkHlp0fd55gCfySXHm_AUDkAifkbb2zJji1pZXtfBMGan1oWhWavqN176hTXdW3dOd6Y723nh6a8E19OBLKDbTpjLOt7YK_Jhe12np78zen5Ovp8XP5kqw-nl-Xi1ViWMlEoiEXBiEtLeNYFlaBzrmpBTKVM24NFAxthlXBRVUK5HWqdcYKlbKc61zX6ZTcnXSjqZ999Cs3_d518aVMoUwhSguM1P2JMq733tla7lwM5gaJIMeu5NiVHLuK7PzEHpqtHf4H5WL9_nC8-AXeSWuz</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>3093003971</pqid></control><display><type>article</type><title>Spectral density estimation for random processes with stationary increments</title><source>Wiley Online Library Journals Frontfile Complete</source><creator>Chen, Wei ; Huang, Chunfeng ; Zhang, Haimeng ; Schaffer, Matthew</creator><creatorcontrib>Chen, Wei ; Huang, Chunfeng ; Zhang, Haimeng ; Schaffer, Matthew</creatorcontrib><description>Spectral density analysis plays an important role in studying a stationary random process on a real line. In this paper, we extend this discussion for the random process with stationary increments. We investigate the properties of the method of moments structure function estimation, and propose a nonparametric spectral density function estimator. Our numerical results show that the proposed spectral density estimator performs comparable with the parametric counterpart when the underlying process is assumed to be band‐limited. Additionally, this method is applied to analyze US Housing Starts Data, where the hidden periodicities are detected, providing consistent conclusions with previous economic studies.</description><identifier>ISSN: 1524-1904</identifier><identifier>EISSN: 1526-4025</identifier><identifier>DOI: 10.1002/asmb.2857</identifier><language>eng</language><publisher>Bognor Regis: Wiley Subscription Services, Inc</publisher><subject>band‐limited spectrum ; Density ; Line spectra ; Method of moments ; nonstationarity ; Random processes ; Spectral density function ; structure function</subject><ispartof>Applied stochastic models in business and industry, 2024-07, Vol.40 (4), p.960-978</ispartof><rights>2024 The Authors. published by John Wiley & Sons Ltd.</rights><rights>2024. This article is published under http://creativecommons.org/licenses/by-nc/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c2927-b067c1039e24198ea0b64cf712a624ec0821e51d847d9714f3bb528a3264b6bf3</cites><orcidid>0000-0002-3753-8088</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1002%2Fasmb.2857$$EPDF$$P50$$Gwiley$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1002%2Fasmb.2857$$EHTML$$P50$$Gwiley$$Hfree_for_read</linktohtml><link.rule.ids>314,778,782,1414,27913,27914,45563,45564</link.rule.ids></links><search><creatorcontrib>Chen, Wei</creatorcontrib><creatorcontrib>Huang, Chunfeng</creatorcontrib><creatorcontrib>Zhang, Haimeng</creatorcontrib><creatorcontrib>Schaffer, Matthew</creatorcontrib><title>Spectral density estimation for random processes with stationary increments</title><title>Applied stochastic models in business and industry</title><description>Spectral density analysis plays an important role in studying a stationary random process on a real line. In this paper, we extend this discussion for the random process with stationary increments. We investigate the properties of the method of moments structure function estimation, and propose a nonparametric spectral density function estimator. Our numerical results show that the proposed spectral density estimator performs comparable with the parametric counterpart when the underlying process is assumed to be band‐limited. Additionally, this method is applied to analyze US Housing Starts Data, where the hidden periodicities are detected, providing consistent conclusions with previous economic studies.</description><subject>band‐limited spectrum</subject><subject>Density</subject><subject>Line spectra</subject><subject>Method of moments</subject><subject>nonstationarity</subject><subject>Random processes</subject><subject>Spectral density function</subject><subject>structure function</subject><issn>1524-1904</issn><issn>1526-4025</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><sourceid>WIN</sourceid><recordid>eNp1kD9PwzAQxS0EEqUw8A0sMTGkvXOcOBlLxT9RxFCYLdtxRKomKbarKt8ep2Vluht-9-69R8gtwgwB2Fz5Vs9YkYkzMsGM5QkHlp0fd55gCfySXHm_AUDkAifkbb2zJji1pZXtfBMGan1oWhWavqN176hTXdW3dOd6Y723nh6a8E19OBLKDbTpjLOt7YK_Jhe12np78zen5Ovp8XP5kqw-nl-Xi1ViWMlEoiEXBiEtLeNYFlaBzrmpBTKVM24NFAxthlXBRVUK5HWqdcYKlbKc61zX6ZTcnXSjqZ999Cs3_d518aVMoUwhSguM1P2JMq733tla7lwM5gaJIMeu5NiVHLuK7PzEHpqtHf4H5WL9_nC8-AXeSWuz</recordid><startdate>202407</startdate><enddate>202407</enddate><creator>Chen, Wei</creator><creator>Huang, Chunfeng</creator><creator>Zhang, Haimeng</creator><creator>Schaffer, Matthew</creator><general>Wiley Subscription Services, Inc</general><scope>24P</scope><scope>WIN</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7TA</scope><scope>8FD</scope><scope>JG9</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><orcidid>https://orcid.org/0000-0002-3753-8088</orcidid></search><sort><creationdate>202407</creationdate><title>Spectral density estimation for random processes with stationary increments</title><author>Chen, Wei ; Huang, Chunfeng ; Zhang, Haimeng ; Schaffer, Matthew</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c2927-b067c1039e24198ea0b64cf712a624ec0821e51d847d9714f3bb528a3264b6bf3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>band‐limited spectrum</topic><topic>Density</topic><topic>Line spectra</topic><topic>Method of moments</topic><topic>nonstationarity</topic><topic>Random processes</topic><topic>Spectral density function</topic><topic>structure function</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chen, Wei</creatorcontrib><creatorcontrib>Huang, Chunfeng</creatorcontrib><creatorcontrib>Zhang, Haimeng</creatorcontrib><creatorcontrib>Schaffer, Matthew</creatorcontrib><collection>Wiley Online Library Open Access</collection><collection>Wiley Free Content</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Materials Business File</collection><collection>Technology Research Database</collection><collection>Materials Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>Applied stochastic models in business and industry</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chen, Wei</au><au>Huang, Chunfeng</au><au>Zhang, Haimeng</au><au>Schaffer, Matthew</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Spectral density estimation for random processes with stationary increments</atitle><jtitle>Applied stochastic models in business and industry</jtitle><date>2024-07</date><risdate>2024</risdate><volume>40</volume><issue>4</issue><spage>960</spage><epage>978</epage><pages>960-978</pages><issn>1524-1904</issn><eissn>1526-4025</eissn><abstract>Spectral density analysis plays an important role in studying a stationary random process on a real line. In this paper, we extend this discussion for the random process with stationary increments. We investigate the properties of the method of moments structure function estimation, and propose a nonparametric spectral density function estimator. Our numerical results show that the proposed spectral density estimator performs comparable with the parametric counterpart when the underlying process is assumed to be band‐limited. Additionally, this method is applied to analyze US Housing Starts Data, where the hidden periodicities are detected, providing consistent conclusions with previous economic studies.</abstract><cop>Bognor Regis</cop><pub>Wiley Subscription Services, Inc</pub><doi>10.1002/asmb.2857</doi><tpages>19</tpages><orcidid>https://orcid.org/0000-0002-3753-8088</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1524-1904 |
| ispartof | Applied stochastic models in business and industry, 2024-07, Vol.40 (4), p.960-978 |
| issn | 1524-1904 1526-4025 |
| language | eng |
| recordid | cdi_proquest_journals_3093003971 |
| source | Wiley Online Library Journals Frontfile Complete |
| subjects | band‐limited spectrum Density Line spectra Method of moments nonstationarity Random processes Spectral density function structure function |
| title | Spectral density estimation for random processes with stationary increments |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-15T09%3A01%3A51IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Spectral%20density%20estimation%20for%20random%20processes%20with%20stationary%20increments&rft.jtitle=Applied%20stochastic%20models%20in%20business%20and%20industry&rft.au=Chen,%20Wei&rft.date=2024-07&rft.volume=40&rft.issue=4&rft.spage=960&rft.epage=978&rft.pages=960-978&rft.issn=1524-1904&rft.eissn=1526-4025&rft_id=info:doi/10.1002/asmb.2857&rft_dat=%3Cproquest_cross%3E3093003971%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=3093003971&rft_id=info:pmid/&rfr_iscdi=true |