Using Bayes methods and mixture models in inter-laboratory studies with outliers
Inter-laboratory studies (especially so-called key comparisons) are conducted to evaluate both national and international equivalence of measurement. In these studies, a reference value of some measurand (the quantity intended to be measured) is developed and results for all laboratories are compare...
Gespeichert in:
Veröffentlicht in: | Accreditation and quality assurance 2010-07, Vol.15 (7), p.379-389 |
---|---|
Hauptverfasser: | , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 389 |
---|---|
container_issue | 7 |
container_start_page | 379 |
container_title | Accreditation and quality assurance |
container_volume | 15 |
creator | Page, Garritt L. Vardeman, Stephen B. |
description | Inter-laboratory studies (especially so-called key comparisons) are conducted to evaluate both national and international equivalence of measurement. In these studies, a reference value of some measurand (the quantity intended to be measured) is developed and results for all laboratories are compared to this single value. How to determine the reference value is not completely obvious if there are observations and/or laboratories that could be considered outliers. Since ignoring results from one or more participating laboratories is untenable in practical terms, developing methods that are robust to the possibility that a small fraction of the laboratories produces observations unlike those from the others is critical. This paper outlines two Bayesian methods of analyzing inter-laboratory data that have been proposed in the literature and suggests three modifications of one that are more robust to outliers. A simulation study is conducted to compare the five methods. |
doi_str_mv | 10.1007/s00769-010-0652-2 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2917923704</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2917923704</sourcerecordid><originalsourceid>FETCH-LOGICAL-c346t-9bb66bf88e2f0f3dbe2dc63ea6157936b38ce508bcf6fade6e52675a2b2a2ad63</originalsourceid><addsrcrecordid>eNp1kEtLAzEUhYMoWKs_wF1AXEbzmCQzSy2-QNCFXYdkkrRT5lGTDNp_b8oUXQmXexf3nHMvHwCXBN8QjOVtzE1UCBOMsOAU0SMwIwWjCHMij8EMV0WFiJT8FJzFuMGY8JKwGXhfxqZfwXu9cxF2Lq0HG6HuLeya7zQGB7vBujbCps-VXECtNkPQaQg7GNNom2z7atIaDmNqGxfiOTjxuo3u4jDnYPn48LF4Rq9vTy-Lu1dUs0IkVBkjhPFl6ajHnlnjqK0Fc1oQLismDCtrx3Fpai-8tk44ToXkmhqqqbaCzcHVlLsNw-foYlKbYQx9PqloRWRFmcRFVpFJVYchxuC82oam02GnCFZ7cGoCpzI4tQenaPZcH5J1rHXrg-7rJv4aKcOSlLzKOjrpYl71Kxf-Pvg__Ac7CX61</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2917923704</pqid></control><display><type>article</type><title>Using Bayes methods and mixture models in inter-laboratory studies with outliers</title><source>ProQuest Central (Alumni Edition)</source><source>Springer Nature - Complete Springer Journals</source><source>ProQuest Central UK/Ireland</source><source>ProQuest Central</source><creator>Page, Garritt L. ; Vardeman, Stephen B.</creator><creatorcontrib>Page, Garritt L. ; Vardeman, Stephen B.</creatorcontrib><description>Inter-laboratory studies (especially so-called key comparisons) are conducted to evaluate both national and international equivalence of measurement. In these studies, a reference value of some measurand (the quantity intended to be measured) is developed and results for all laboratories are compared to this single value. How to determine the reference value is not completely obvious if there are observations and/or laboratories that could be considered outliers. Since ignoring results from one or more participating laboratories is untenable in practical terms, developing methods that are robust to the possibility that a small fraction of the laboratories produces observations unlike those from the others is critical. This paper outlines two Bayesian methods of analyzing inter-laboratory data that have been proposed in the literature and suggests three modifications of one that are more robust to outliers. A simulation study is conducted to compare the five methods.</description><identifier>ISSN: 0949-1775</identifier><identifier>EISSN: 1432-0517</identifier><identifier>DOI: 10.1007/s00769-010-0652-2</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer-Verlag</publisher><subject>Analytical Chemistry ; Bayesian analysis ; Biochemistry ; Chemistry ; Chemistry and Materials Science ; Commercial Law ; Ecotoxicology ; Exact sciences and technology ; Food Science ; General Paper ; Laboratories ; Marketing ; Outliers (statistics) ; Robustness ; Sample variance</subject><ispartof>Accreditation and quality assurance, 2010-07, Vol.15 (7), p.379-389</ispartof><rights>Springer-Verlag 2010</rights><rights>2015 INIST-CNRS</rights><rights>Springer-Verlag 2010.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c346t-9bb66bf88e2f0f3dbe2dc63ea6157936b38ce508bcf6fade6e52675a2b2a2ad63</citedby><cites>FETCH-LOGICAL-c346t-9bb66bf88e2f0f3dbe2dc63ea6157936b38ce508bcf6fade6e52675a2b2a2ad63</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00769-010-0652-2$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2917923704?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,778,782,21377,21378,27913,27914,33519,33733,41477,42546,43648,43794,51308,64372,64376,72228</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=23071859$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Page, Garritt L.</creatorcontrib><creatorcontrib>Vardeman, Stephen B.</creatorcontrib><title>Using Bayes methods and mixture models in inter-laboratory studies with outliers</title><title>Accreditation and quality assurance</title><addtitle>Accred Qual Assur</addtitle><description>Inter-laboratory studies (especially so-called key comparisons) are conducted to evaluate both national and international equivalence of measurement. In these studies, a reference value of some measurand (the quantity intended to be measured) is developed and results for all laboratories are compared to this single value. How to determine the reference value is not completely obvious if there are observations and/or laboratories that could be considered outliers. Since ignoring results from one or more participating laboratories is untenable in practical terms, developing methods that are robust to the possibility that a small fraction of the laboratories produces observations unlike those from the others is critical. This paper outlines two Bayesian methods of analyzing inter-laboratory data that have been proposed in the literature and suggests three modifications of one that are more robust to outliers. A simulation study is conducted to compare the five methods.</description><subject>Analytical Chemistry</subject><subject>Bayesian analysis</subject><subject>Biochemistry</subject><subject>Chemistry</subject><subject>Chemistry and Materials Science</subject><subject>Commercial Law</subject><subject>Ecotoxicology</subject><subject>Exact sciences and technology</subject><subject>Food Science</subject><subject>General Paper</subject><subject>Laboratories</subject><subject>Marketing</subject><subject>Outliers (statistics)</subject><subject>Robustness</subject><subject>Sample variance</subject><issn>0949-1775</issn><issn>1432-0517</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNp1kEtLAzEUhYMoWKs_wF1AXEbzmCQzSy2-QNCFXYdkkrRT5lGTDNp_b8oUXQmXexf3nHMvHwCXBN8QjOVtzE1UCBOMsOAU0SMwIwWjCHMij8EMV0WFiJT8FJzFuMGY8JKwGXhfxqZfwXu9cxF2Lq0HG6HuLeya7zQGB7vBujbCps-VXECtNkPQaQg7GNNom2z7atIaDmNqGxfiOTjxuo3u4jDnYPn48LF4Rq9vTy-Lu1dUs0IkVBkjhPFl6ajHnlnjqK0Fc1oQLismDCtrx3Fpai-8tk44ToXkmhqqqbaCzcHVlLsNw-foYlKbYQx9PqloRWRFmcRFVpFJVYchxuC82oam02GnCFZ7cGoCpzI4tQenaPZcH5J1rHXrg-7rJv4aKcOSlLzKOjrpYl71Kxf-Pvg__Ac7CX61</recordid><startdate>20100701</startdate><enddate>20100701</enddate><creator>Page, Garritt L.</creator><creator>Vardeman, Stephen B.</creator><general>Springer-Verlag</general><general>Springer</general><general>Springer Nature B.V</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7XB</scope><scope>88C</scope><scope>8FE</scope><scope>8FG</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>HCIFZ</scope><scope>KB.</scope><scope>M0T</scope><scope>PDBOC</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope></search><sort><creationdate>20100701</creationdate><title>Using Bayes methods and mixture models in inter-laboratory studies with outliers</title><author>Page, Garritt L. ; Vardeman, Stephen B.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c346t-9bb66bf88e2f0f3dbe2dc63ea6157936b38ce508bcf6fade6e52675a2b2a2ad63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2010</creationdate><topic>Analytical Chemistry</topic><topic>Bayesian analysis</topic><topic>Biochemistry</topic><topic>Chemistry</topic><topic>Chemistry and Materials Science</topic><topic>Commercial Law</topic><topic>Ecotoxicology</topic><topic>Exact sciences and technology</topic><topic>Food Science</topic><topic>General Paper</topic><topic>Laboratories</topic><topic>Marketing</topic><topic>Outliers (statistics)</topic><topic>Robustness</topic><topic>Sample variance</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Page, Garritt L.</creatorcontrib><creatorcontrib>Vardeman, Stephen B.</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Healthcare Administration Database (Alumni)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>SciTech Premium Collection</collection><collection>Materials Science Database</collection><collection>Healthcare Administration Database</collection><collection>Materials Science Collection</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><jtitle>Accreditation and quality assurance</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Page, Garritt L.</au><au>Vardeman, Stephen B.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Using Bayes methods and mixture models in inter-laboratory studies with outliers</atitle><jtitle>Accreditation and quality assurance</jtitle><stitle>Accred Qual Assur</stitle><date>2010-07-01</date><risdate>2010</risdate><volume>15</volume><issue>7</issue><spage>379</spage><epage>389</epage><pages>379-389</pages><issn>0949-1775</issn><eissn>1432-0517</eissn><abstract>Inter-laboratory studies (especially so-called key comparisons) are conducted to evaluate both national and international equivalence of measurement. In these studies, a reference value of some measurand (the quantity intended to be measured) is developed and results for all laboratories are compared to this single value. How to determine the reference value is not completely obvious if there are observations and/or laboratories that could be considered outliers. Since ignoring results from one or more participating laboratories is untenable in practical terms, developing methods that are robust to the possibility that a small fraction of the laboratories produces observations unlike those from the others is critical. This paper outlines two Bayesian methods of analyzing inter-laboratory data that have been proposed in the literature and suggests three modifications of one that are more robust to outliers. A simulation study is conducted to compare the five methods.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer-Verlag</pub><doi>10.1007/s00769-010-0652-2</doi><tpages>11</tpages></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0949-1775 |
ispartof | Accreditation and quality assurance, 2010-07, Vol.15 (7), p.379-389 |
issn | 0949-1775 1432-0517 |
language | eng |
recordid | cdi_proquest_journals_2917923704 |
source | ProQuest Central (Alumni Edition); Springer Nature - Complete Springer Journals; ProQuest Central UK/Ireland; ProQuest Central |
subjects | Analytical Chemistry Bayesian analysis Biochemistry Chemistry Chemistry and Materials Science Commercial Law Ecotoxicology Exact sciences and technology Food Science General Paper Laboratories Marketing Outliers (statistics) Robustness Sample variance |
title | Using Bayes methods and mixture models in inter-laboratory studies with outliers |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-15T07%3A51%3A29IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Using%20Bayes%20methods%20and%20mixture%20models%20in%20inter-laboratory%20studies%20with%20outliers&rft.jtitle=Accreditation%20and%20quality%20assurance&rft.au=Page,%20Garritt%20L.&rft.date=2010-07-01&rft.volume=15&rft.issue=7&rft.spage=379&rft.epage=389&rft.pages=379-389&rft.issn=0949-1775&rft.eissn=1432-0517&rft_id=info:doi/10.1007/s00769-010-0652-2&rft_dat=%3Cproquest_cross%3E2917923704%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2917923704&rft_id=info:pmid/&rfr_iscdi=true |