Closed subset selection procedures for selecting good populations
Identifieur interne : 001575 ( Istex/Corpus ); précédent : 001574; suivant : 001576Closed subset selection procedures for selecting good populations
Auteurs : H. Finner ; G. GianiSource :
- Journal of Statistical Planning and Inference [ 0378-3758 ] ; 1994.
Abstract
The problem of selecting a subset containing all good treatments is considered. It is shown that under mild conditions the class of procedures deciding correctly with at least a prespecified probability P* is equivalent in some sense to the class of all consonant and coherent multiple tests at multiple level 1 − P* for a certain system of hypotheses. The equivalence relationship can be used to construct the so-called closed subset selection procedures by applying the well-known principle of closed test procedures. These procedures are more powerful than their single-step counterparts, the so-called natural selection procedures, in the sense that the selected subset of a closed selection procedure is never larger but often smaller than the selected subset of the corresponding natural decision rule. For a sampling statistic having a Lebesgue density with location parameter, analytical results are given concerning the critical values necessary to carry out the closed subset selection procedure. The normal case with both known and unknown common variance is treated in detail.
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DOI: 10.1016/0378-3758(94)90034-5
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Giani</name><affiliation><mods:affiliation>Abteilung Biometrie und Epidemiologie, Diabetes-Forschungsinstitut an der Universität Düsseldorf, Auf'm Hennekamp 65, D-4000 Düsseldorf 1, Germany</mods:affiliation></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">ISTEX</idno><idno type="RBID">ISTEX:68630E12A560B28F29068BC9643FD943A62982B1</idno><date when="1994" year="1994">1994</date><idno type="doi">10.1016/0378-3758(94)90034-5</idno><idno type="url">https://api.istex.fr/document/68630E12A560B28F29068BC9643FD943A62982B1/fulltext/pdf</idno><idno type="wicri:Area/Istex/Corpus">001575</idno><idno type="wicri:explorRef" wicri:stream="Istex" wicri:step="Corpus" wicri:corpus="ISTEX">001575</idno></publicationStmt><sourceDesc><biblStruct><analytic><title level="a">Closed subset selection procedures for selecting good populations</title><author><name sortKey="Finner, H" sort="Finner, H" uniqKey="Finner H" first="H." last="Finner">H. Finner</name><affiliation><mods:affiliation>FB IV — Mathematik/Statistik, Universität Trier, Postfach 3825, D-5500 Trier, Germany</mods:affiliation></affiliation></author><author><name sortKey="Giani, G" sort="Giani, G" uniqKey="Giani G" first="G." last="Giani">G. Giani</name><affiliation><mods:affiliation>Abteilung Biometrie und Epidemiologie, Diabetes-Forschungsinstitut an der Universität Düsseldorf, Auf'm Hennekamp 65, D-4000 Düsseldorf 1, Germany</mods:affiliation></affiliation></author></analytic><monogr></monogr><series><title level="j">Journal of Statistical Planning and Inference</title><title level="j" type="abbrev">JSPI</title><idno type="ISSN">0378-3758</idno><imprint><publisher>ELSEVIER</publisher><date type="published" when="1994">1994</date><biblScope unit="volume">38</biblScope><biblScope unit="issue">2</biblScope><biblScope unit="page" from="179">179</biblScope><biblScope unit="page" to="199">199</biblScope></imprint><idno type="ISSN">0378-3758</idno></series><idno type="istex">68630E12A560B28F29068BC9643FD943A62982B1</idno><idno type="DOI">10.1016/0378-3758(94)90034-5</idno><idno type="PII">0378-3758(94)90034-5</idno></biblStruct></sourceDesc><seriesStmt><idno type="ISSN">0378-3758</idno></seriesStmt></fileDesc><profileDesc><textClass></textClass><langUsage><language ident="en">en</language></langUsage></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">The problem of selecting a subset containing all good treatments is considered. It is shown that under mild conditions the class of procedures deciding correctly with at least a prespecified probability P* is equivalent in some sense to the class of all consonant and coherent multiple tests at multiple level 1 − P* for a certain system of hypotheses. The equivalence relationship can be used to construct the so-called closed subset selection procedures by applying the well-known principle of closed test procedures. These procedures are more powerful than their single-step counterparts, the so-called natural selection procedures, in the sense that the selected subset of a closed selection procedure is never larger but often smaller than the selected subset of the corresponding natural decision rule. For a sampling statistic having a Lebesgue density with location parameter, analytical results are given concerning the critical values necessary to carry out the closed subset selection procedure. The normal case with both known and unknown common variance is treated in detail.</div></front></TEI><istex><corpusName>elsevier</corpusName><author><json:item><name>H. Finner</name><affiliations><json:string>FB IV — Mathematik/Statistik, Universität Trier, Postfach 3825, D-5500 Trier, Germany</json:string></affiliations></json:item><json:item><name>G. 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It is shown that under mild conditions the class of procedures deciding correctly with at least a prespecified probability P* is equivalent in some sense to the class of all consonant and coherent multiple tests at multiple level 1 − P* for a certain system of hypotheses. The equivalence relationship can be used to construct the so-called closed subset selection procedures by applying the well-known principle of closed test procedures. These procedures are more powerful than their single-step counterparts, the so-called natural selection procedures, in the sense that the selected subset of a closed selection procedure is never larger but often smaller than the selected subset of the corresponding natural decision rule. For a sampling statistic having a Lebesgue density with location parameter, analytical results are given concerning the critical values necessary to carry out the closed subset selection procedure. The normal case with both known and unknown common variance is treated in detail.</abstract><qualityIndicators><score>6.992</score><pdfVersion>1.2</pdfVersion><pdfPageSize>468 x 684 pts</pdfPageSize><refBibsNative>true</refBibsNative><keywordCount>9</keywordCount><abstractCharCount>1088</abstractCharCount><pdfWordCount>6326</pdfWordCount><pdfCharCount>45163</pdfCharCount><pdfPageCount>21</pdfPageCount><abstractWordCount>166</abstractWordCount></qualityIndicators><title>Closed subset selection procedures for selecting good populations</title><pii><json:string>0378-3758(94)90034-5</json:string></pii><refBibs><json:item><author><json:item><name>P. Bauer</name></json:item><json:item><name>P. Hackl</name></json:item><json:item><name>G. Hommel</name></json:item><json:item><name>E. Sonnemann</name></json:item></author><host><volume>33</volume><pages><last>127</last><first>121</first></pages><author></author><title>Metrika</title></host><title>Multiple testing of pairs of one-sided hypotheses</title></json:item><json:item><author><json:item><name>R.E. Bechhofer</name></json:item></author><host><volume>25</volume><pages><last>39</last><first>16</first></pages><author></author><title>Ann. Math. Statist.</title></host><title>A single-sample multiple decision procedure for ranking means of normal populations with known variances</title></json:item><json:item><author><json:item><name>G. Broström</name></json:item></author><host><volume>A10</volume><pages><last>221</last><first>203</first></pages><author></author><title>Commun. Statist. Theor. Meth.</title></host><title>On sequentially rejective subset selection procedures</title></json:item><json:item><author><json:item><name>H.A. David</name></json:item><json:item><name>P.A. Lachenbruch</name></json:item><json:item><name>H.P. Brandis</name></json:item></author><host><volume>59</volume><pages><last>168</last><first>161</first></pages><author></author><title>Biometrika</title></host><title>The power function of range and studentized range tests in normal samples</title></json:item><json:item><author><json:item><name>M.M. Desu</name></json:item></author><host><volume>41</volume><pages><last>1603</last><first>1596</first></pages><author></author><title>Ann. Math. Statist.</title></host><title>A selection problem</title></json:item><json:item><author><json:item><name>H. Finner</name></json:item></author><host><author></author><title>Multiple Hypothesenprüfung (Multiple Hypotheses Testing)</title></host><serie><author></author><title>Multiple Hypothesenprüfung (Multiple Hypotheses Testing)</title></serie><title>Multiple Tests und Fehler III. Part (Multiple tests and directional errors)</title></json:item><json:item><author><json:item><name>H. Finner</name></json:item></author><host><volume>19</volume><pages><last>53</last><first>41</first></pages><author></author><title>Commun. Statist. Theor. Meth.</title></host><title>On the modified S-method and directional errors</title></json:item><json:item><author><json:item><name>K.R. Gabriel</name></json:item></author><host><volume>40</volume><pages><last>250</last><first>224</first></pages><author></author><title>Ann. Math. Statist.</title></host><title>Simultaneous test procedures — some theory of multiple comparisons</title></json:item><json:item><author><json:item><name>G. Giani</name></json:item><json:item><name>H. Finner</name></json:item></author><host><volume>28</volume><pages><last>47</last><first>33</first></pages><author></author><title>J. Statist. Planning Infer.</title></host><title>Some general results on least favorable configurations with special reference to equivalence testing and the range statistic</title></json:item><json:item><author><json:item><name>S.S. Gupta</name></json:item><json:item><name>M. Sobel</name></json:item></author><host><volume>29</volume><pages><last>244</last><first>235</first></pages><author></author><title>Ann. Math. Statist.</title></host><title>On selecting a subset which contains all populations better than a standard</title></json:item><json:item><author><json:item><name>A.J. Hayter</name></json:item></author><host><volume>12</volume><pages><last>75</last><first>61</first></pages><author></author><title>Ann. Statist.</title></host><title>A proof of the conjecture that the Tukey–Kramer multiple comparison procedure is conservative</title></json:item><json:item><host><author></author><title>A stagewise directional test based on t-statistics</title></host></json:item><json:item><author><json:item><name>J. Kunert</name></json:item></author><host><volume>85</volume><pages><last>812</last><first>808</first></pages><author></author><title>J. Amer. Statist. Assoc.</title></host><title>On the power of tests for multiple comparisons of three normal means</title></json:item><json:item><author><json:item><name>K. Lam</name></json:item></author><host><volume>73</volume><pages><last>206</last><first>201</first></pages><author></author><title>Biometrika</title></host><title>A new procedure for selecting good populations</title></json:item><json:item><author><json:item><name>E.L. Lehmann</name></json:item></author><host><volume>32</volume><pages><last>1012</last><first>990</first></pages><author></author><title>Ann. Math. Statist.</title></host><title>Some model I problems of selection</title></json:item><json:item><author><json:item><name>L.-Y. Lin</name></json:item><json:item><name>B.-C. Jen</name></json:item></author><host><volume>13</volume><pages><last>195</last><first>179</first></pages><author></author><title>Soochow J. Math.</title></host><title>A procedure for the selection of populations with means fall within normal limits</title></json:item><json:item><author><json:item><name>R. Marcus</name></json:item><json:item><name>E. Peritz</name></json:item><json:item><name>K.R. Gabriel</name></json:item></author><host><volume>63</volume><pages><last>660</last><first>655</first></pages><author></author><title>Biometrika</title></host><title>On closed testing procedures with special reference to ordered analysis of variance</title></json:item><json:item><author><json:item><name>U.D. Naik</name></json:item></author><host><volume>4</volume><pages><last>535</last><first>519</first></pages><author></author><title>Comm. Statist.</title></host><title>Some selection rules for comparing p processes with a standard</title></json:item><json:item><author><json:item><name>U.D. Naik</name></json:item></author><host><volume>A6</volume><pages><last>966</last><first>955</first></pages><author></author><title>Commun. Statist. Theor. Meth.</title></host><title>Some subset selection problems</title></json:item><json:item><author><json:item><name>A. Prekopa</name></json:item></author><host><volume>34</volume><pages><last>343</last><first>335</first></pages><author></author><title>Acta Sci. Math.</title></host><title>On logarithmic concave measures and functions</title></json:item><json:item><author><json:item><name>P.S. Puri</name></json:item><json:item><name>M.L. Puri</name></json:item></author><host><volume>30</volume><pages><last>302</last><first>291</first></pages><author></author><title>Sankhyā Ser. A</title></host><title>Selection procedures based on ranks: scale parameter case</title></json:item><json:item><author><json:item><name>M.L. Puri</name></json:item><json:item><name>P.S. Puri</name></json:item></author><host><volume>40</volume><pages><last>632</last><first>619</first></pages><author></author><title>Ann. Math. Statist.</title></host><title>Multiple decision procedures based on ranks for certain problems in analysis of variance</title></json:item><json:item><author><json:item><name>M.H. Rizvi</name></json:item><json:item><name>M. Sobel</name></json:item><json:item><name>G.G. Woodworth</name></json:item></author><host><volume>39</volume><pages><last>2093</last><first>2075</first></pages><author></author><title>Ann. Math. Statist.</title></host><title>Nonparametric ranking procedures for comparison with a control</title></json:item><json:item><author><json:item><name>J.P. Shaffer</name></json:item></author><host><volume>8</volume><pages><last>1347</last><first>1342</first></pages><author></author><title>Ann. Statist.</title></host><title>Control of directional errors with stagewise multiple test procedures</title></json:item><json:item><author><json:item><name>J.P. Shaffer</name></json:item></author><host><volume>76</volume><pages><last>401</last><first>395</first></pages><author></author><title>J. Amer. Statist. Assoc.</title></host><title>Complexity: an interpretability criterion for multiple comparisons</title></json:item><json:item><author><json:item><name>B.W. Turnbull</name></json:item></author><host><volume>A5</volume><pages><last>1244</last><first>1225</first></pages><author></author><title>Comm. Statist. Theor. Meth.</title></host><title>Multiple decision rules for comparing several populations with a fixed known standard</title></json:item><json:item><author><json:item><name>E. Sonnemann</name></json:item></author><host><volume>13</volume><pages><last>128</last><first>120</first></pages><author></author><title>EDV in Med. u. Biol.</title></host><title>Allgemeine Lösungen multipler Testprobleme</title></json:item><json:item><host><author></author><title>The Multivariate Normal Distribution</title></host></json:item></refBibs><genre><json:string>research-article</json:string></genre><serie><editor><json:item><name>P. Bauer</name></json:item></editor><pages><last>153</last><first>144</first></pages><language><json:string>unknown</json:string></language><title>Multiple Hypothesenprüfung (Multiple Hypotheses Testing)</title></serie><host><volume>38</volume><pii><json:string>S0378-3758(00)X0118-8</json:string></pii><pages><last>199</last><first>179</first></pages><issn><json:string>0378-3758</json:string></issn><issue>2</issue><genre><json:string>journal</json:string></genre><language><json:string>unknown</json:string></language><title>Journal of Statistical Planning and Inference</title><publicationDate>1994</publicationDate></host><categories><wos><json:string>science</json:string><json:string>statistics & probability</json:string></wos><scienceMetrix><json:string>natural sciences</json:string><json:string>mathematics & statistics</json:string><json:string>statistics & probability</json:string></scienceMetrix></categories><publicationDate>1994</publicationDate><copyrightDate>1994</copyrightDate><doi><json:string>10.1016/0378-3758(94)90034-5</json:string></doi><id>68630E12A560B28F29068BC9643FD943A62982B1</id><score>0.48130092</score><fulltext><json:item><extension>pdf</extension><original>true</original><mimetype>application/pdf</mimetype><uri>https://api.istex.fr/document/68630E12A560B28F29068BC9643FD943A62982B1/fulltext/pdf</uri></json:item><json:item><extension>zip</extension><original>false</original><mimetype>application/zip</mimetype><uri>https://api.istex.fr/document/68630E12A560B28F29068BC9643FD943A62982B1/fulltext/zip</uri></json:item><istex:fulltextTEI uri="https://api.istex.fr/document/68630E12A560B28F29068BC9643FD943A62982B1/fulltext/tei"><teiHeader><fileDesc><titleStmt><title level="a">Closed subset selection procedures for selecting good populations</title></titleStmt><publicationStmt><authority>ISTEX</authority><publisher>ELSEVIER</publisher><availability><p>ELSEVIER</p></availability><date>1994</date></publicationStmt><sourceDesc><biblStruct type="inbook"><analytic><title level="a">Closed subset selection procedures for selecting good populations</title><author xml:id="author-1"><persName><forename type="first">H.</forename><surname>Finner</surname></persName><affiliation>FB IV — Mathematik/Statistik, Universität Trier, Postfach 3825, D-5500 Trier, Germany</affiliation></author><author xml:id="author-2"><persName><forename type="first">G.</forename><surname>Giani</surname></persName><note type="correspondence"><p>Correspondence to: Dr. G. Giani, Abteilung Biometrie und Epidemiologie, Diabetes-Forschungsinstitut an der Universität Düsseldorf, Auf'm Hennekamp 65, D-4000 Düsseldorf 1, Germany.</p></note><affiliation>Abteilung Biometrie und Epidemiologie, Diabetes-Forschungsinstitut an der Universität Düsseldorf, Auf'm Hennekamp 65, D-4000 Düsseldorf 1, Germany</affiliation></author></analytic><monogr><title level="j">Journal of Statistical Planning and Inference</title><title level="j" type="abbrev">JSPI</title><idno type="pISSN">0378-3758</idno><idno type="PII">S0378-3758(00)X0118-8</idno><imprint><publisher>ELSEVIER</publisher><date type="published" when="1994"></date><biblScope unit="volume">38</biblScope><biblScope unit="issue">2</biblScope><biblScope unit="page" from="179">179</biblScope><biblScope unit="page" to="199">199</biblScope></imprint></monogr><idno type="istex">68630E12A560B28F29068BC9643FD943A62982B1</idno><idno type="DOI">10.1016/0378-3758(94)90034-5</idno><idno type="PII">0378-3758(94)90034-5</idno></biblStruct></sourceDesc></fileDesc><profileDesc><creation><date>1994</date></creation><langUsage><language ident="en">en</language></langUsage><abstract xml:lang="en"><p>The problem of selecting a subset containing all good treatments is considered. It is shown that under mild conditions the class of procedures deciding correctly with at least a prespecified probability P* is equivalent in some sense to the class of all consonant and coherent multiple tests at multiple level 1 − P* for a certain system of hypotheses. The equivalence relationship can be used to construct the so-called closed subset selection procedures by applying the well-known principle of closed test procedures. These procedures are more powerful than their single-step counterparts, the so-called natural selection procedures, in the sense that the selected subset of a closed selection procedure is never larger but often smaller than the selected subset of the corresponding natural decision rule. For a sampling statistic having a Lebesgue density with location parameter, analytical results are given concerning the critical values necessary to carry out the closed subset selection procedure. The normal case with both known and unknown common variance is treated in detail.</p></abstract><textClass><keywords scheme="keyword"><list><head>MSC</head><item><term>Primary 62F07, 62J15</term></item><item><term>secondary 62C05, 62E99</term></item></list></keywords></textClass><textClass><keywords scheme="keyword"><list><head>Keywords</head><item><term>Closed test procedure</term></item><item><term>least favourable parameter configuration</term></item><item><term>multiple test</term></item><item><term>normal distribution</term></item><item><term>range statistic</term></item><item><term>studentized range</term></item><item><term>subset selection</term></item></list></keywords></textClass></profileDesc><revisionDesc><change when="1994">Published</change></revisionDesc></teiHeader></istex:fulltextTEI><json:item><extension>txt</extension><original>false</original><mimetype>text/plain</mimetype><uri>https://api.istex.fr/document/68630E12A560B28F29068BC9643FD943A62982B1/fulltext/txt</uri></json:item></fulltext><metadata><istex:metadataXml wicri:clean="Elsevier, elements deleted: tail"><istex:xmlDeclaration>version="1.0" encoding="utf-8"</istex:xmlDeclaration><istex:docType PUBLIC="-//ES//DTD journal article DTD version 4.5.2//EN//XML" URI="art452.dtd" name="istex:docType"></istex:docType><istex:document><converted-article version="4.5.2" docsubtype="fla"><item-info><jid>JSPI</jid><aid>94900345</aid><ce:pii>0378-3758(94)90034-5</ce:pii><ce:doi>10.1016/0378-3758(94)90034-5</ce:doi><ce:copyright type="unknown" year="1994"></ce:copyright></item-info><head><ce:title>Closed subset selection procedures for selecting good populations</ce:title><ce:author-group><ce:author><ce:given-name>H.</ce:given-name><ce:surname>Finner</ce:surname></ce:author><ce:affiliation><ce:textfn>FB IV — Mathematik/Statistik, Universität Trier, Postfach 3825, D-5500 Trier, Germany</ce:textfn></ce:affiliation></ce:author-group><ce:author-group><ce:author><ce:given-name>G.</ce:given-name><ce:surname>Giani</ce:surname><ce:cross-ref refid="COR1"><ce:sup>∗</ce:sup></ce:cross-ref></ce:author><ce:affiliation><ce:textfn>Abteilung Biometrie und Epidemiologie, Diabetes-Forschungsinstitut an der Universität Düsseldorf, Auf'm Hennekamp 65, D-4000 Düsseldorf 1, Germany</ce:textfn></ce:affiliation><ce:correspondence id="COR1"><ce:label>∗</ce:label><ce:text>Correspondence to: Dr. G. Giani, Abteilung Biometrie und Epidemiologie, Diabetes-Forschungsinstitut an der Universität Düsseldorf, Auf'm Hennekamp 65, D-4000 Düsseldorf 1, Germany.</ce:text></ce:correspondence></ce:author-group><ce:abstract><ce:section-title>Abstract</ce:section-title><ce:abstract-sec><ce:simple-para>The problem of selecting a subset containing all good treatments is considered. It is shown that under mild conditions the class of procedures deciding correctly with at least a prespecified probability <ce:italic>P</ce:italic><ce:sup>*</ce:sup> is equivalent in some sense to the class of all consonant and coherent multiple tests at multiple level 1 − <ce:italic>P</ce:italic><ce:sup>*</ce:sup> for a certain system of hypotheses. The equivalence relationship can be used to construct the so-called closed subset selection procedures by applying the well-known principle of closed test procedures. These procedures are more powerful than their single-step counterparts, the so-called natural selection procedures, in the sense that the selected subset of a closed selection procedure is never larger but often smaller than the selected subset of the corresponding natural decision rule. For a sampling statistic having a Lebesgue density with location parameter, analytical results are given concerning the critical values necessary to carry out the closed subset selection procedure. The normal case with both known and unknown common variance is treated in detail.</ce:simple-para></ce:abstract-sec></ce:abstract><ce:keywords class="msc"><ce:section-title>MSC</ce:section-title><ce:keyword><ce:text>Primary 62F07, 62J15</ce:text></ce:keyword><ce:keyword><ce:text>secondary 62C05, 62E99</ce:text></ce:keyword></ce:keywords><ce:keywords><ce:section-title>Keywords</ce:section-title><ce:keyword><ce:text>Closed test procedure</ce:text></ce:keyword><ce:keyword><ce:text>least favourable parameter configuration</ce:text></ce:keyword><ce:keyword><ce:text>multiple test</ce:text></ce:keyword><ce:keyword><ce:text>normal distribution</ce:text></ce:keyword><ce:keyword><ce:text>range statistic</ce:text></ce:keyword><ce:keyword><ce:text>studentized range</ce:text></ce:keyword><ce:keyword><ce:text>subset selection</ce:text></ce:keyword></ce:keywords></head></converted-article></istex:document></istex:metadataXml><mods version="3.6"><titleInfo><title>Closed subset selection procedures for selecting good populations</title></titleInfo><titleInfo type="alternative" contentType="CDATA"><title>Closed subset selection procedures for selecting good populations</title></titleInfo><name type="personal"><namePart type="given">H.</namePart><namePart type="family">Finner</namePart><affiliation>FB IV — Mathematik/Statistik, Universität Trier, Postfach 3825, D-5500 Trier, Germany</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">G.</namePart><namePart type="family">Giani</namePart><affiliation>Abteilung Biometrie und Epidemiologie, Diabetes-Forschungsinstitut an der Universität Düsseldorf, Auf'm Hennekamp 65, D-4000 Düsseldorf 1, Germany</affiliation><description>Correspondence to: Dr. G. Giani, Abteilung Biometrie und Epidemiologie, Diabetes-Forschungsinstitut an der Universität Düsseldorf, Auf'm Hennekamp 65, D-4000 Düsseldorf 1, Germany.</description><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="research-article" displayLabel="Full-length article"></genre><originInfo><publisher>ELSEVIER</publisher><dateIssued encoding="w3cdtf">1994</dateIssued><copyrightDate encoding="w3cdtf">1994</copyrightDate></originInfo><language><languageTerm type="code" authority="iso639-2b">eng</languageTerm><languageTerm type="code" authority="rfc3066">en</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType></physicalDescription><abstract lang="en">The problem of selecting a subset containing all good treatments is considered. It is shown that under mild conditions the class of procedures deciding correctly with at least a prespecified probability P* is equivalent in some sense to the class of all consonant and coherent multiple tests at multiple level 1 − P* for a certain system of hypotheses. The equivalence relationship can be used to construct the so-called closed subset selection procedures by applying the well-known principle of closed test procedures. These procedures are more powerful than their single-step counterparts, the so-called natural selection procedures, in the sense that the selected subset of a closed selection procedure is never larger but often smaller than the selected subset of the corresponding natural decision rule. For a sampling statistic having a Lebesgue density with location parameter, analytical results are given concerning the critical values necessary to carry out the closed subset selection procedure. The normal case with both known and unknown common variance is treated in detail.</abstract><subject><genre>MSC</genre><topic>Primary 62F07, 62J15</topic><topic>secondary 62C05, 62E99</topic></subject><subject><genre>Keywords</genre><topic>Closed test procedure</topic><topic>least favourable parameter configuration</topic><topic>multiple test</topic><topic>normal distribution</topic><topic>range statistic</topic><topic>studentized range</topic><topic>subset selection</topic></subject><relatedItem type="host"><titleInfo><title>Journal of Statistical Planning and Inference</title></titleInfo><titleInfo type="abbreviated"><title>JSPI</title></titleInfo><genre type="journal">journal</genre><originInfo><dateIssued encoding="w3cdtf">199402</dateIssued></originInfo><identifier type="ISSN">0378-3758</identifier><identifier type="PII">S0378-3758(00)X0118-8</identifier><part><date>199402</date><detail type="volume"><number>38</number><caption>vol.</caption></detail><detail type="issue"><number>2</number><caption>no.</caption></detail><extent unit="issue pages"><start>141</start><end>278</end></extent><extent unit="pages"><start>179</start><end>199</end></extent></part></relatedItem><identifier type="istex">68630E12A560B28F29068BC9643FD943A62982B1</identifier><identifier type="DOI">10.1016/0378-3758(94)90034-5</identifier><identifier type="PII">0378-3758(94)90034-5</identifier><recordInfo><recordContentSource>ELSEVIER</recordContentSource></recordInfo></mods></metadata></istex></record>Pour manipuler ce document sous Unix (Dilib)
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