On Use of the Em Algorithm for Penalized Likelihood Estimation
SUMMARY The EM algorithm is a popular approach to maximum likelihood estimation but has not been much used for penalized likelihood or maximum a posteriori estimation. This paper discusses properties of the EM algorithm in such contexts, concentrating on rates of convergence, and presents an alterna...
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| Veröffentlicht in: | Journal of the Royal Statistical Society. Series B, Methodological Methodological, 1990, Vol.52 (3), p.443-452 |
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| container_end_page | 452 |
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| container_issue | 3 |
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| container_title | Journal of the Royal Statistical Society. Series B, Methodological |
| container_volume | 52 |
| creator | Green, Peter J. |
| description | SUMMARY
The EM algorithm is a popular approach to maximum likelihood estimation but has not been much used for penalized likelihood or maximum a posteriori estimation. This paper discusses properties of the EM algorithm in such contexts, concentrating on rates of convergence, and presents an alternative that is usually more practical and converges at least as quickly. |
| doi_str_mv | 10.1111/j.2517-6161.1990.tb01798.x |
| format | Article |
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The EM algorithm is a popular approach to maximum likelihood estimation but has not been much used for penalized likelihood or maximum a posteriori estimation. This paper discusses properties of the EM algorithm in such contexts, concentrating on rates of convergence, and presents an alternative that is usually more practical and converges at least as quickly.</description><identifier>ISSN: 0035-9246</identifier><identifier>ISSN: 1369-7412</identifier><identifier>EISSN: 2517-6161</identifier><identifier>EISSN: 1467-9868</identifier><identifier>DOI: 10.1111/j.2517-6161.1990.tb01798.x</identifier><identifier>CODEN: JSTBAJ</identifier><language>eng</language><publisher>London: Royal Statistical Society</publisher><subject>convergence rates ; em algorithm ; Exact sciences and technology ; Mathematics ; maximum a posteriori estimation ; Nonparametric inference ; one‐step‐late algorithm, penalized likelihood ; Probability and statistics ; Sciences and techniques of general use ; Statistics</subject><ispartof>Journal of the Royal Statistical Society. Series B, Methodological, 1990, Vol.52 (3), p.443-452</ispartof><rights>1990 Royal Statistical Society</rights><rights>1991 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c4068-b2a81d531e860c871defddeb6c99b250170d8f4a9e465c583ff87c8f2ca6318b3</citedby><cites>FETCH-LOGICAL-c4068-b2a81d531e860c871defddeb6c99b250170d8f4a9e465c583ff87c8f2ca6318b3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,4010,27846,27900,27901,27902</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=19321451$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Green, Peter J.</creatorcontrib><title>On Use of the Em Algorithm for Penalized Likelihood Estimation</title><title>Journal of the Royal Statistical Society. Series B, Methodological</title><description>SUMMARY
The EM algorithm is a popular approach to maximum likelihood estimation but has not been much used for penalized likelihood or maximum a posteriori estimation. This paper discusses properties of the EM algorithm in such contexts, concentrating on rates of convergence, and presents an alternative that is usually more practical and converges at least as quickly.</description><subject>convergence rates</subject><subject>em algorithm</subject><subject>Exact sciences and technology</subject><subject>Mathematics</subject><subject>maximum a posteriori estimation</subject><subject>Nonparametric inference</subject><subject>one‐step‐late algorithm, penalized likelihood</subject><subject>Probability and statistics</subject><subject>Sciences and techniques of general use</subject><subject>Statistics</subject><issn>0035-9246</issn><issn>1369-7412</issn><issn>2517-6161</issn><issn>1467-9868</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1990</creationdate><recordtype>article</recordtype><sourceid>K30</sourceid><recordid>eNqVkF9LwzAUxYMoOKffISg-tiZNmyY-CHPMPzBQnHsOaZq41K6ZSYebn97WDX32vtwL99x7Dj8AzjGKcVdXVZxkOI8opjjGnKO4LRDOOYs3B2DwuzoEA4RIFvEkpcfgJIQKIYRJSgbg5qmB86ChM7BdaDhZwlH95rxtF0tonIfPupG1_dIlnNp3XduFcyWchNYuZWtdcwqOjKyDPtv3IZjfTV7HD9H06f5xPJpGKkWURUUiGS4zgjWjSLEcl9qUpS6o4rxIsi4yKplJJdcpzVTGiDEsV8wkSlKCWUGG4GL3d-Xdx1qHVlRu7btoQWCCEp7lOU061fVOpbwLwWsjVr4L6rcCI9HzEpXooYgeiuh5iT0vsemOL_cWMihZGy8bZcPfB04SnGa40412uk9b6-0_HMTLbHb7M5Nv-lh-6A</recordid><startdate>1990</startdate><enddate>1990</enddate><creator>Green, Peter J.</creator><general>Royal Statistical Society</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>HGTKA</scope><scope>JILTI</scope><scope>K30</scope><scope>PAAUG</scope><scope>PAWHS</scope><scope>PAWZZ</scope><scope>PAXOH</scope><scope>PBHAV</scope><scope>PBQSW</scope><scope>PBYQZ</scope><scope>PCIWU</scope><scope>PCMID</scope><scope>PCZJX</scope><scope>PDGRG</scope><scope>PDWWI</scope><scope>PETMR</scope><scope>PFVGT</scope><scope>PGXDX</scope><scope>PIHIL</scope><scope>PISVA</scope><scope>PJCTQ</scope><scope>PJTMS</scope><scope>PLCHJ</scope><scope>PMHAD</scope><scope>PNQDJ</scope><scope>POUND</scope><scope>PPLAD</scope><scope>PQAPC</scope><scope>PQCAN</scope><scope>PQCMW</scope><scope>PQEME</scope><scope>PQHKH</scope><scope>PQMID</scope><scope>PQNCT</scope><scope>PQNET</scope><scope>PQSCT</scope><scope>PQSET</scope><scope>PSVJG</scope><scope>PVMQY</scope><scope>PZGFC</scope></search><sort><creationdate>1990</creationdate><title>On Use of the Em Algorithm for Penalized Likelihood Estimation</title><author>Green, Peter J.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c4068-b2a81d531e860c871defddeb6c99b250170d8f4a9e465c583ff87c8f2ca6318b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1990</creationdate><topic>convergence rates</topic><topic>em algorithm</topic><topic>Exact sciences and technology</topic><topic>Mathematics</topic><topic>maximum a posteriori estimation</topic><topic>Nonparametric inference</topic><topic>one‐step‐late algorithm, penalized likelihood</topic><topic>Probability and statistics</topic><topic>Sciences and techniques of general use</topic><topic>Statistics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Green, Peter J.</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Periodicals Index Online Segment 18</collection><collection>Periodicals Index Online Segment 32</collection><collection>Periodicals Index Online</collection><collection>Primary Sources Access—Foundation Edition (Plan E) - 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Series B, Methodological</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Green, Peter J.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>On Use of the Em Algorithm for Penalized Likelihood Estimation</atitle><jtitle>Journal of the Royal Statistical Society. Series B, Methodological</jtitle><date>1990</date><risdate>1990</risdate><volume>52</volume><issue>3</issue><spage>443</spage><epage>452</epage><pages>443-452</pages><issn>0035-9246</issn><issn>1369-7412</issn><eissn>2517-6161</eissn><eissn>1467-9868</eissn><coden>JSTBAJ</coden><abstract>SUMMARY
The EM algorithm is a popular approach to maximum likelihood estimation but has not been much used for penalized likelihood or maximum a posteriori estimation. This paper discusses properties of the EM algorithm in such contexts, concentrating on rates of convergence, and presents an alternative that is usually more practical and converges at least as quickly.</abstract><cop>London</cop><pub>Royal Statistical Society</pub><doi>10.1111/j.2517-6161.1990.tb01798.x</doi><tpages>10</tpages></addata></record> |
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| identifier | ISSN: 0035-9246 |
| ispartof | Journal of the Royal Statistical Society. Series B, Methodological, 1990, Vol.52 (3), p.443-452 |
| issn | 0035-9246 1369-7412 2517-6161 1467-9868 |
| language | eng |
| recordid | cdi_proquest_journals_1302957762 |
| source | Jstor Complete Legacy; Periodicals Index Online; JSTOR Mathematics & Statistics |
| subjects | convergence rates em algorithm Exact sciences and technology Mathematics maximum a posteriori estimation Nonparametric inference one‐step‐late algorithm, penalized likelihood Probability and statistics Sciences and techniques of general use Statistics |
| title | On Use of the Em Algorithm for Penalized Likelihood Estimation |
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