Nonparametric Detection of Signals by Information Theoretic Criteria: Performance Analysis and an Improved Estimator
Determining the number of sources from observed data is a fundamental problem in many scientific fields. In this paper we consider the nonparametric setting, and focus on the detection performance of two popular estimators based on information theoretic criteria, the Akaike information criterion (AI...
Gespeichert in:
| Veröffentlicht in: | IEEE transactions on signal processing 2010-05, Vol.58 (5), p.2746-2756 |
|---|---|
| 1. Verfasser: | |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext bestellen |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 2756 |
|---|---|
| container_issue | 5 |
| container_start_page | 2746 |
| container_title | IEEE transactions on signal processing |
| container_volume | 58 |
| creator | Nadler, Boaz |
| description | Determining the number of sources from observed data is a fundamental problem in many scientific fields. In this paper we consider the nonparametric setting, and focus on the detection performance of two popular estimators based on information theoretic criteria, the Akaike information criterion (AIC) and minimum description length (MDL). We present three contributions on this subject. First, we derive a new expression for the detection performance of the MDL estimator, which exhibits a much closer fit to simulations in comparison to previous formulas. Second, we present a random matrix theory viewpoint of the performance of the AIC estimator, including approximate analytical formulas for its overestimation probability. Finally, we show that a small increase in the penalty term of AIC leads to an estimator with a very good detection performance and a negligible overestimation probability. |
| doi_str_mv | 10.1109/TSP.2010.2042481 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_RIE</sourceid><recordid>TN_cdi_proquest_journals_1027168368</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>5406089</ieee_id><sourcerecordid>753734809</sourcerecordid><originalsourceid>FETCH-LOGICAL-c461t-dba398c495f6baf36c8b3d121bfa4ba633639bd04a49e16809146f0bd08e2acf3</originalsourceid><addsrcrecordid>eNpdkUtrGzEUhUVpIamTfaAbQSlZTaLXaEbdBSdtDaEJ2IHsBo3mKlGwR64kF_zve_0gi4KEHvc7B10dQi44u-KcmevF_PFKMDwJpoRq-Qdyyo3iFVON_oh7VsuqbpvnE_I55zfGuFJGn5LyO45rm-wKSgqO3kIBV0IcafR0Hl5Gu8y039LZ6GNa2X1l8QoxQUF6mkKBFOx3-ghpD4wO6A2KtjlkascBJ52t1in-hYHe5RLQI6Yz8smjMZwf1wl5-nG3mP6q7h9-zqY395VTmpdq6K00rVOm9rq3XmrX9nLggvfeqt5qKbU0_cCUVQa4bpnhSnuGNy0I67yckMuDLz7gzwZy6VYhO1gu7Qhxk7umlo1UqEPy63_kW9ykXfcdZ6JBc4ljQtiBcinmnMB364QdpS1C3S6FDlPodil0xxRQ8u1obLOzS5_wi0J-1wnRiNbwnfWXAxcA4L1cK6ZZa-Q_0bSRfQ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1027168368</pqid></control><display><type>article</type><title>Nonparametric Detection of Signals by Information Theoretic Criteria: Performance Analysis and an Improved Estimator</title><source>IEEE Electronic Library (IEL)</source><creator>Nadler, Boaz</creator><creatorcontrib>Nadler, Boaz</creatorcontrib><description>Determining the number of sources from observed data is a fundamental problem in many scientific fields. In this paper we consider the nonparametric setting, and focus on the detection performance of two popular estimators based on information theoretic criteria, the Akaike information criterion (AIC) and minimum description length (MDL). We present three contributions on this subject. First, we derive a new expression for the detection performance of the MDL estimator, which exhibits a much closer fit to simulations in comparison to previous formulas. Second, we present a random matrix theory viewpoint of the performance of the AIC estimator, including approximate analytical formulas for its overestimation probability. Finally, we show that a small increase in the penalty term of AIC leads to an estimator with a very good detection performance and a negligible overestimation probability.</description><identifier>ISSN: 1053-587X</identifier><identifier>EISSN: 1941-0476</identifier><identifier>DOI: 10.1109/TSP.2010.2042481</identifier><identifier>CODEN: ITPRED</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Applied sciences ; Approximation ; Array signal processing ; Computer science ; Covariance matrix ; Criteria ; Detection, estimation, filtering, equalization, prediction ; Eigenvalues and eigenfunctions ; Estimators ; Exact sciences and technology ; Information theoretic criteria ; Information theory ; Information, signal and communications theory ; Mathematical analysis ; Mathematics ; Matrix theory ; Miscellaneous ; Performance analysis ; Probability ; random matrix theory ; Sensor arrays ; Signal analysis ; Signal and communications theory ; Signal detection ; Signal processing ; Signal, noise ; Simulation ; source enumeration ; Telecommunications and information theory</subject><ispartof>IEEE transactions on signal processing, 2010-05, Vol.58 (5), p.2746-2756</ispartof><rights>2015 INIST-CNRS</rights><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) May 2010</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c461t-dba398c495f6baf36c8b3d121bfa4ba633639bd04a49e16809146f0bd08e2acf3</citedby><cites>FETCH-LOGICAL-c461t-dba398c495f6baf36c8b3d121bfa4ba633639bd04a49e16809146f0bd08e2acf3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/5406089$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>314,780,784,796,27923,27924,54757</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/5406089$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=22728918$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Nadler, Boaz</creatorcontrib><title>Nonparametric Detection of Signals by Information Theoretic Criteria: Performance Analysis and an Improved Estimator</title><title>IEEE transactions on signal processing</title><addtitle>TSP</addtitle><description>Determining the number of sources from observed data is a fundamental problem in many scientific fields. In this paper we consider the nonparametric setting, and focus on the detection performance of two popular estimators based on information theoretic criteria, the Akaike information criterion (AIC) and minimum description length (MDL). We present three contributions on this subject. First, we derive a new expression for the detection performance of the MDL estimator, which exhibits a much closer fit to simulations in comparison to previous formulas. Second, we present a random matrix theory viewpoint of the performance of the AIC estimator, including approximate analytical formulas for its overestimation probability. Finally, we show that a small increase in the penalty term of AIC leads to an estimator with a very good detection performance and a negligible overestimation probability.</description><subject>Applied sciences</subject><subject>Approximation</subject><subject>Array signal processing</subject><subject>Computer science</subject><subject>Covariance matrix</subject><subject>Criteria</subject><subject>Detection, estimation, filtering, equalization, prediction</subject><subject>Eigenvalues and eigenfunctions</subject><subject>Estimators</subject><subject>Exact sciences and technology</subject><subject>Information theoretic criteria</subject><subject>Information theory</subject><subject>Information, signal and communications theory</subject><subject>Mathematical analysis</subject><subject>Mathematics</subject><subject>Matrix theory</subject><subject>Miscellaneous</subject><subject>Performance analysis</subject><subject>Probability</subject><subject>random matrix theory</subject><subject>Sensor arrays</subject><subject>Signal analysis</subject><subject>Signal and communications theory</subject><subject>Signal detection</subject><subject>Signal processing</subject><subject>Signal, noise</subject><subject>Simulation</subject><subject>source enumeration</subject><subject>Telecommunications and information theory</subject><issn>1053-587X</issn><issn>1941-0476</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkUtrGzEUhUVpIamTfaAbQSlZTaLXaEbdBSdtDaEJ2IHsBo3mKlGwR64kF_zve_0gi4KEHvc7B10dQi44u-KcmevF_PFKMDwJpoRq-Qdyyo3iFVON_oh7VsuqbpvnE_I55zfGuFJGn5LyO45rm-wKSgqO3kIBV0IcafR0Hl5Gu8y039LZ6GNa2X1l8QoxQUF6mkKBFOx3-ghpD4wO6A2KtjlkascBJ52t1in-hYHe5RLQI6Yz8smjMZwf1wl5-nG3mP6q7h9-zqY395VTmpdq6K00rVOm9rq3XmrX9nLggvfeqt5qKbU0_cCUVQa4bpnhSnuGNy0I67yckMuDLz7gzwZy6VYhO1gu7Qhxk7umlo1UqEPy63_kW9ykXfcdZ6JBc4ljQtiBcinmnMB364QdpS1C3S6FDlPodil0xxRQ8u1obLOzS5_wi0J-1wnRiNbwnfWXAxcA4L1cK6ZZa-Q_0bSRfQ</recordid><startdate>20100501</startdate><enddate>20100501</enddate><creator>Nadler, Boaz</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>F28</scope><scope>FR3</scope></search><sort><creationdate>20100501</creationdate><title>Nonparametric Detection of Signals by Information Theoretic Criteria: Performance Analysis and an Improved Estimator</title><author>Nadler, Boaz</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c461t-dba398c495f6baf36c8b3d121bfa4ba633639bd04a49e16809146f0bd08e2acf3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Applied sciences</topic><topic>Approximation</topic><topic>Array signal processing</topic><topic>Computer science</topic><topic>Covariance matrix</topic><topic>Criteria</topic><topic>Detection, estimation, filtering, equalization, prediction</topic><topic>Eigenvalues and eigenfunctions</topic><topic>Estimators</topic><topic>Exact sciences and technology</topic><topic>Information theoretic criteria</topic><topic>Information theory</topic><topic>Information, signal and communications theory</topic><topic>Mathematical analysis</topic><topic>Mathematics</topic><topic>Matrix theory</topic><topic>Miscellaneous</topic><topic>Performance analysis</topic><topic>Probability</topic><topic>random matrix theory</topic><topic>Sensor arrays</topic><topic>Signal analysis</topic><topic>Signal and communications theory</topic><topic>Signal detection</topic><topic>Signal processing</topic><topic>Signal, noise</topic><topic>Simulation</topic><topic>source enumeration</topic><topic>Telecommunications and information theory</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Nadler, Boaz</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology 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><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><jtitle>IEEE transactions on signal processing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Nadler, Boaz</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Nonparametric Detection of Signals by Information Theoretic Criteria: Performance Analysis and an Improved Estimator</atitle><jtitle>IEEE transactions on signal processing</jtitle><stitle>TSP</stitle><date>2010-05-01</date><risdate>2010</risdate><volume>58</volume><issue>5</issue><spage>2746</spage><epage>2756</epage><pages>2746-2756</pages><issn>1053-587X</issn><eissn>1941-0476</eissn><coden>ITPRED</coden><abstract>Determining the number of sources from observed data is a fundamental problem in many scientific fields. In this paper we consider the nonparametric setting, and focus on the detection performance of two popular estimators based on information theoretic criteria, the Akaike information criterion (AIC) and minimum description length (MDL). We present three contributions on this subject. First, we derive a new expression for the detection performance of the MDL estimator, which exhibits a much closer fit to simulations in comparison to previous formulas. Second, we present a random matrix theory viewpoint of the performance of the AIC estimator, including approximate analytical formulas for its overestimation probability. Finally, we show that a small increase in the penalty term of AIC leads to an estimator with a very good detection performance and a negligible overestimation probability.</abstract><cop>New York, NY</cop><pub>IEEE</pub><doi>10.1109/TSP.2010.2042481</doi><tpages>11</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | ISSN: 1053-587X |
| ispartof | IEEE transactions on signal processing, 2010-05, Vol.58 (5), p.2746-2756 |
| issn | 1053-587X 1941-0476 |
| language | eng |
| recordid | cdi_proquest_journals_1027168368 |
| source | IEEE Electronic Library (IEL) |
| subjects | Applied sciences Approximation Array signal processing Computer science Covariance matrix Criteria Detection, estimation, filtering, equalization, prediction Eigenvalues and eigenfunctions Estimators Exact sciences and technology Information theoretic criteria Information theory Information, signal and communications theory Mathematical analysis Mathematics Matrix theory Miscellaneous Performance analysis Probability random matrix theory Sensor arrays Signal analysis Signal and communications theory Signal detection Signal processing Signal, noise Simulation source enumeration Telecommunications and information theory |
| title | Nonparametric Detection of Signals by Information Theoretic Criteria: Performance Analysis and an Improved Estimator |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-12T17%3A15%3A53IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_RIE&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Nonparametric%20Detection%20of%20Signals%20by%20Information%20Theoretic%20Criteria:%20Performance%20Analysis%20and%20an%20Improved%20Estimator&rft.jtitle=IEEE%20transactions%20on%20signal%20processing&rft.au=Nadler,%20Boaz&rft.date=2010-05-01&rft.volume=58&rft.issue=5&rft.spage=2746&rft.epage=2756&rft.pages=2746-2756&rft.issn=1053-587X&rft.eissn=1941-0476&rft.coden=ITPRED&rft_id=info:doi/10.1109/TSP.2010.2042481&rft_dat=%3Cproquest_RIE%3E753734809%3C/proquest_RIE%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1027168368&rft_id=info:pmid/&rft_ieee_id=5406089&rfr_iscdi=true |