Dual divergence estimators and tests: Robustness results
The class of dual ϕ -divergence estimators (introduced in Broniatowski and Keziou (2009) [5]) is explored with respect to robustness through the influence function approach. For scale and location models, this class is investigated in terms of robustness and asymptotic relative efficiency. Some hyp...
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Veröffentlicht in: | Journal of multivariate analysis 2011-01, Vol.102 (1), p.20-36 |
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container_title | Journal of multivariate analysis |
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creator | Toma, Aida Broniatowski, Michel |
description | The class of dual
ϕ
-divergence estimators (introduced in Broniatowski and Keziou (2009)
[5]) is explored with respect to robustness through the influence function approach. For scale and location models, this class is investigated in terms of robustness and asymptotic relative efficiency. Some hypothesis tests based on dual divergence criteria are proposed and their robustness properties are studied. The empirical performances of these estimators and tests are illustrated by Monte Carlo simulation for both non-contaminated and contaminated data. |
doi_str_mv | 10.1016/j.jmva.2010.07.010 |
format | Article |
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-divergence estimators (introduced in Broniatowski and Keziou (2009)
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ϕ
-divergence estimators (introduced in Broniatowski and Keziou (2009)
[5]) is explored with respect to robustness through the influence function approach. For scale and location models, this class is investigated in terms of robustness and asymptotic relative efficiency. Some hypothesis tests based on dual divergence criteria are proposed and their robustness properties are studied. The empirical performances of these estimators and tests are illustrated by Monte Carlo simulation for both non-contaminated and contaminated data.</description><subject>Distribution theory</subject><subject>Estimating techniques</subject><subject>Exact sciences and technology</subject><subject>Location model</subject><subject>Location model Minimum divergence estimators Robust estimation Robust test Scale model</subject><subject>Mathematical functions</subject><subject>Mathematics</subject><subject>Minimum divergence estimators</subject><subject>Monte Carlo simulation</subject><subject>Multivariate analysis</subject><subject>Nonparametric inference</subject><subject>Parametric inference</subject><subject>Probability and statistics</subject><subject>Robust estimation</subject><subject>Robust test</subject><subject>Scale model</subject><subject>Sciences and techniques of general use</subject><subject>Statistics</subject><subject>Statistics Theory</subject><subject>Studies</subject><issn>0047-259X</issn><issn>1095-7243</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><sourceid>X2L</sourceid><recordid>eNp9UU2L1TAUDaLgc_QPuCqCCxd93qRJkyduhvFjBh4IouAupMmtk9LXPpO2MP_eWzu8pYubQy7nHE5OGHvNYc-B1--7fXda3F4ALUDvCZ6wHYeDKrWQ1VO2A5C6FOrw6zl7kXMHwLnScsfMp9n1RYgLpt84eCwwT_HkpjHlwg2hmOiePxTfx2bO04A5Fwnz3E_5JXvWuj7jq0e8Yj-_fP5xc1sev329u7k-ll4Bn0ovXTAenVIV1KAPjQmuDgKEQe8b0QIEZbTkplJcqtbotqmdaoCLxiuUobpi7zbfe9fbc6Js6cGOLtrb66Ndd_QyybmQiyDum417TuOfmZLbbpzTQPGsVqYW3NSKSGIj-TTmnLC9uHKwa5m2s2uZdi3TgrYEJLrbRAnP6C8KRFypg7OLrRwHQecDDUk5QVyXNOd_K1vV9n46kdfbx5Que9e3yQ0-5ounqLSqtJTE-7jxkOpdIiabfVz_KMSEfrJhjP-L_Bd6TKGu</recordid><startdate>20110101</startdate><enddate>20110101</enddate><creator>Toma, Aida</creator><creator>Broniatowski, Michel</creator><general>Elsevier Inc</general><general>Elsevier</general><general>Taylor & Francis LLC</general><scope>6I.</scope><scope>AAFTH</scope><scope>IQODW</scope><scope>DKI</scope><scope>X2L</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>JQ2</scope><scope>1XC</scope><scope>VOOES</scope><orcidid>https://orcid.org/0000-0001-6301-5531</orcidid></search><sort><creationdate>20110101</creationdate><title>Dual divergence estimators and tests: Robustness results</title><author>Toma, Aida ; Broniatowski, Michel</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c501t-c4ad8cea55306079b8da6d2028eccb2f00d58741835145f87fb6a5b012bc5e4d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Distribution theory</topic><topic>Estimating techniques</topic><topic>Exact sciences and technology</topic><topic>Location model</topic><topic>Location model Minimum divergence estimators Robust estimation Robust test Scale model</topic><topic>Mathematical functions</topic><topic>Mathematics</topic><topic>Minimum divergence estimators</topic><topic>Monte Carlo simulation</topic><topic>Multivariate analysis</topic><topic>Nonparametric inference</topic><topic>Parametric inference</topic><topic>Probability and statistics</topic><topic>Robust estimation</topic><topic>Robust test</topic><topic>Scale model</topic><topic>Sciences and techniques of general use</topic><topic>Statistics</topic><topic>Statistics Theory</topic><topic>Studies</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Toma, Aida</creatorcontrib><creatorcontrib>Broniatowski, Michel</creatorcontrib><collection>ScienceDirect Open Access Titles</collection><collection>Elsevier:ScienceDirect:Open Access</collection><collection>Pascal-Francis</collection><collection>RePEc IDEAS</collection><collection>RePEc</collection><collection>CrossRef</collection><collection>ProQuest Computer Science Collection</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>Hyper Article en Ligne (HAL) (Open Access)</collection><jtitle>Journal of multivariate analysis</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Toma, Aida</au><au>Broniatowski, Michel</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Dual divergence estimators and tests: Robustness results</atitle><jtitle>Journal of multivariate analysis</jtitle><date>2011-01-01</date><risdate>2011</risdate><volume>102</volume><issue>1</issue><spage>20</spage><epage>36</epage><pages>20-36</pages><issn>0047-259X</issn><eissn>1095-7243</eissn><coden>JMVAAI</coden><abstract>The class of dual
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-divergence estimators (introduced in Broniatowski and Keziou (2009)
[5]) is explored with respect to robustness through the influence function approach. For scale and location models, this class is investigated in terms of robustness and asymptotic relative efficiency. Some hypothesis tests based on dual divergence criteria are proposed and their robustness properties are studied. The empirical performances of these estimators and tests are illustrated by Monte Carlo simulation for both non-contaminated and contaminated data.</abstract><cop>Amsterdam</cop><pub>Elsevier Inc</pub><doi>10.1016/j.jmva.2010.07.010</doi><tpages>17</tpages><orcidid>https://orcid.org/0000-0001-6301-5531</orcidid><oa>free_for_read</oa></addata></record> |
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source | RePEc; Access via ScienceDirect (Elsevier); EZB-FREE-00999 freely available EZB journals |
subjects | Distribution theory Estimating techniques Exact sciences and technology Location model Location model Minimum divergence estimators Robust estimation Robust test Scale model Mathematical functions Mathematics Minimum divergence estimators Monte Carlo simulation Multivariate analysis Nonparametric inference Parametric inference Probability and statistics Robust estimation Robust test Scale model Sciences and techniques of general use Statistics Statistics Theory Studies |
title | Dual divergence estimators and tests: Robustness results |
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