Loading…

On MDL Estimation for Simple Contaminated Gaussian Location Families

The performance of MDL density estimators defined as the minimizer of two part code lengths is guaranteed in terms of the redundancy of the two part code [2], [3]. When the true density belongs to the assumed model, the redundancy of a code can be bounded by the regret (pointwise redundancy) of the...

Full description

Saved in:
Bibliographic Details
Main Authors: Miyamoto, Kohei, Takeuchi, Jun'ichi
Format: Conference Proceeding
Language:English
Subjects:
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page 591
container_issue
container_start_page 587
container_title
container_volume
creator Miyamoto, Kohei
Takeuchi, Jun'ichi
description The performance of MDL density estimators defined as the minimizer of two part code lengths is guaranteed in terms of the redundancy of the two part code [2], [3]. When the true density belongs to the assumed model, the redundancy of a code can be bounded by the regret (pointwise redundancy) of the code. Then, the construction of two part codes which achieve small regret based on quantization of parametric family is developed. For exponential families, it is known that we can achieve sufficiently small regret by using this construction [4]. For non-exponential families, the evaluation of the regret achieved by using this construction breaks. However, for non-exponential families under certain assumptions, by enhancing this construction using local exponentially family bundles [1], we can design efficient two part codes [9]. In this paper, we show that we can apply this coding method to contamination model [5] with simple settings and give the guarantee of performance of MDL estimators for them.
format conference_proceeding
fullrecord <record><control><sourceid>ieee_CHZPO</sourceid><recordid>TN_cdi_ieee_primary_9366112</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>9366112</ieee_id><sourcerecordid>9366112</sourcerecordid><originalsourceid>FETCH-LOGICAL-i105t-d1c717cc6345e8b353ff02d43fe60b765de13e21fbc007774655978c1971f1393</originalsourceid><addsrcrecordid>eNotjstKAzEYRqMgWGufwE1eYCDJn-tSpheFKV2o65LJ_IHITKZM4sK3t1BX3-IcDt8deZLWKiUAGNyTldDWNcoq-Ug2pXwzxkAwcNKuyPaU6XHb0V2pafI1zZnGeaEfabqMSNs5Vz-l7CsO9OB_Skk-024ON3N_ZWPC8kweoh8Lbv53Tb72u8_2relOh_f2tWsSZ6o2Aw-GmxA0SIW2BwUxMjFIiKhZb7QakAMKHvvAmDFGaqWcsYE7wyMHB2vycusmRDxfluvj5ffsQGvOBfwBL-VEOQ</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>On MDL Estimation for Simple Contaminated Gaussian Location Families</title><source>IEEE Xplore All Conference Series</source><creator>Miyamoto, Kohei ; Takeuchi, Jun'ichi</creator><creatorcontrib>Miyamoto, Kohei ; Takeuchi, Jun'ichi</creatorcontrib><description>The performance of MDL density estimators defined as the minimizer of two part code lengths is guaranteed in terms of the redundancy of the two part code [2], [3]. When the true density belongs to the assumed model, the redundancy of a code can be bounded by the regret (pointwise redundancy) of the code. Then, the construction of two part codes which achieve small regret based on quantization of parametric family is developed. For exponential families, it is known that we can achieve sufficiently small regret by using this construction [4]. For non-exponential families, the evaluation of the regret achieved by using this construction breaks. However, for non-exponential families under certain assumptions, by enhancing this construction using local exponentially family bundles [1], we can design efficient two part codes [9]. In this paper, we show that we can apply this coding method to contamination model [5] with simple settings and give the guarantee of performance of MDL estimators for them.</description><identifier>EISSN: 2689-5854</identifier><identifier>EISBN: 4885523303</identifier><identifier>EISBN: 9784885523304</identifier><language>eng</language><publisher>IEICE</publisher><subject>Contamination ; Encoding ; Estimation ; Quantization (signal) ; Redundancy</subject><ispartof>2020 International Symposium on Information Theory and Its Applications (ISITA), 2020, p.587-591</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9366112$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,23910,23911,25119,54534,54911</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9366112$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Miyamoto, Kohei</creatorcontrib><creatorcontrib>Takeuchi, Jun'ichi</creatorcontrib><title>On MDL Estimation for Simple Contaminated Gaussian Location Families</title><title>2020 International Symposium on Information Theory and Its Applications (ISITA)</title><addtitle>ISITA</addtitle><description>The performance of MDL density estimators defined as the minimizer of two part code lengths is guaranteed in terms of the redundancy of the two part code [2], [3]. When the true density belongs to the assumed model, the redundancy of a code can be bounded by the regret (pointwise redundancy) of the code. Then, the construction of two part codes which achieve small regret based on quantization of parametric family is developed. For exponential families, it is known that we can achieve sufficiently small regret by using this construction [4]. For non-exponential families, the evaluation of the regret achieved by using this construction breaks. However, for non-exponential families under certain assumptions, by enhancing this construction using local exponentially family bundles [1], we can design efficient two part codes [9]. In this paper, we show that we can apply this coding method to contamination model [5] with simple settings and give the guarantee of performance of MDL estimators for them.</description><subject>Contamination</subject><subject>Encoding</subject><subject>Estimation</subject><subject>Quantization (signal)</subject><subject>Redundancy</subject><issn>2689-5854</issn><isbn>4885523303</isbn><isbn>9784885523304</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2020</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotjstKAzEYRqMgWGufwE1eYCDJn-tSpheFKV2o65LJ_IHITKZM4sK3t1BX3-IcDt8deZLWKiUAGNyTldDWNcoq-Ug2pXwzxkAwcNKuyPaU6XHb0V2pafI1zZnGeaEfabqMSNs5Vz-l7CsO9OB_Skk-024ON3N_ZWPC8kweoh8Lbv53Tb72u8_2relOh_f2tWsSZ6o2Aw-GmxA0SIW2BwUxMjFIiKhZb7QakAMKHvvAmDFGaqWcsYE7wyMHB2vycusmRDxfluvj5ffsQGvOBfwBL-VEOQ</recordid><startdate>20201024</startdate><enddate>20201024</enddate><creator>Miyamoto, Kohei</creator><creator>Takeuchi, Jun'ichi</creator><general>IEICE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>20201024</creationdate><title>On MDL Estimation for Simple Contaminated Gaussian Location Families</title><author>Miyamoto, Kohei ; Takeuchi, Jun'ichi</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i105t-d1c717cc6345e8b353ff02d43fe60b765de13e21fbc007774655978c1971f1393</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Contamination</topic><topic>Encoding</topic><topic>Estimation</topic><topic>Quantization (signal)</topic><topic>Redundancy</topic><toplevel>online_resources</toplevel><creatorcontrib>Miyamoto, Kohei</creatorcontrib><creatorcontrib>Takeuchi, Jun'ichi</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Miyamoto, Kohei</au><au>Takeuchi, Jun'ichi</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>On MDL Estimation for Simple Contaminated Gaussian Location Families</atitle><btitle>2020 International Symposium on Information Theory and Its Applications (ISITA)</btitle><stitle>ISITA</stitle><date>2020-10-24</date><risdate>2020</risdate><spage>587</spage><epage>591</epage><pages>587-591</pages><eissn>2689-5854</eissn><eisbn>4885523303</eisbn><eisbn>9784885523304</eisbn><abstract>The performance of MDL density estimators defined as the minimizer of two part code lengths is guaranteed in terms of the redundancy of the two part code [2], [3]. When the true density belongs to the assumed model, the redundancy of a code can be bounded by the regret (pointwise redundancy) of the code. Then, the construction of two part codes which achieve small regret based on quantization of parametric family is developed. For exponential families, it is known that we can achieve sufficiently small regret by using this construction [4]. For non-exponential families, the evaluation of the regret achieved by using this construction breaks. However, for non-exponential families under certain assumptions, by enhancing this construction using local exponentially family bundles [1], we can design efficient two part codes [9]. In this paper, we show that we can apply this coding method to contamination model [5] with simple settings and give the guarantee of performance of MDL estimators for them.</abstract><pub>IEICE</pub><tpages>5</tpages></addata></record>
fulltext fulltext_linktorsrc
identifier EISSN: 2689-5854
ispartof 2020 International Symposium on Information Theory and Its Applications (ISITA), 2020, p.587-591
issn 2689-5854
language eng
recordid cdi_ieee_primary_9366112
source IEEE Xplore All Conference Series
subjects Contamination
Encoding
Estimation
Quantization (signal)
Redundancy
title On MDL Estimation for Simple Contaminated Gaussian Location Families
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-22T12%3A11%3A30IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_CHZPO&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=On%20MDL%20Estimation%20for%20Simple%20Contaminated%20Gaussian%20Location%20Families&rft.btitle=2020%20International%20Symposium%20on%20Information%20Theory%20and%20Its%20Applications%20(ISITA)&rft.au=Miyamoto,%20Kohei&rft.date=2020-10-24&rft.spage=587&rft.epage=591&rft.pages=587-591&rft.eissn=2689-5854&rft_id=info:doi/&rft.eisbn=4885523303&rft.eisbn_list=9784885523304&rft_dat=%3Cieee_CHZPO%3E9366112%3C/ieee_CHZPO%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-i105t-d1c717cc6345e8b353ff02d43fe60b765de13e21fbc007774655978c1971f1393%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=9366112&rfr_iscdi=true