A Note on Bayesian Design for the Normal Linear Model with Unknown Error Variance
The Bayesian theory of optimal experimental design for the normal linear model has been developed under the assumption of known variance. The insensitivity of specific design criteria to prior assumptions on the variance distribution has been demonstrated in special cases, but a general result showi...
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| Veröffentlicht in: | Biometrika 2000-03, Vol.87 (1), p.222-227 |
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| container_end_page | 227 |
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| container_issue | 1 |
| container_start_page | 222 |
| container_title | Biometrika |
| container_volume | 87 |
| creator | Verdinelli, Isabella |
| description | The Bayesian theory of optimal experimental design for the normal linear model has been developed under the assumption of known variance. The insensitivity of specific design criteria to prior assumptions on the variance distribution has been demonstrated in special cases, but a general result showing the way in which Bayesian optimal designs are affected by prior information on the variance is lacking. This note proves that Bayesian designs are insensitive to information about the variance in a more general way than previously thought. |
| doi_str_mv | 10.1093/biomet/87.1.222 |
| format | Article |
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This note proves that Bayesian designs are insensitive to information about the variance in a more general way than previously thought.</description><identifier>ISSN: 0006-3444</identifier><identifier>EISSN: 1464-3510</identifier><identifier>DOI: 10.1093/biomet/87.1.222</identifier><identifier>CODEN: BIOKAX</identifier><language>eng</language><publisher>Oxford: Biometrika Trust</publisher><subject>Bayesian theories ; Covariance matrices ; Distribution theory ; Exact sciences and technology ; Expected utility ; Experiment design ; Linear models ; Linear regression ; Mathematics ; Matrices ; Miscellanea ; Parametric models ; Probability and statistics ; Regression analysis ; Sciences and techniques of general use ; Statistical variance ; Statistics</subject><ispartof>Biometrika, 2000-03, Vol.87 (1), p.222-227</ispartof><rights>Copyright 2000 Biometrika Trust</rights><rights>2000 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.jstor.org/stable/pdf/2673577$$EPDF$$P50$$Gjstor$$H</linktopdf><linktohtml>$$Uhttps://www.jstor.org/stable/2673577$$EHTML$$P50$$Gjstor$$H</linktohtml><link.rule.ids>314,780,784,803,832,27923,27924,58016,58020,58249,58253</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=1308376$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Verdinelli, Isabella</creatorcontrib><title>A Note on Bayesian Design for the Normal Linear Model with Unknown Error Variance</title><title>Biometrika</title><description>The Bayesian theory of optimal experimental design for the normal linear model has been developed under the assumption of known variance. 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This note proves that Bayesian designs are insensitive to information about the variance in a more general way than previously thought.</description><subject>Bayesian theories</subject><subject>Covariance matrices</subject><subject>Distribution theory</subject><subject>Exact sciences and technology</subject><subject>Expected utility</subject><subject>Experiment design</subject><subject>Linear models</subject><subject>Linear regression</subject><subject>Mathematics</subject><subject>Matrices</subject><subject>Miscellanea</subject><subject>Parametric models</subject><subject>Probability and statistics</subject><subject>Regression analysis</subject><subject>Sciences and techniques of general use</subject><subject>Statistical variance</subject><subject>Statistics</subject><issn>0006-3444</issn><issn>1464-3510</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2000</creationdate><recordtype>article</recordtype><recordid>eNo9jc1LAzEUxIMouFbPXjzk4HXbl-_0WGv9gKoI1mvJZrN2121SkoXS_95IxXcZHvObGYSuCYwJTNmkasPWDROtxmRMKT1BBeGSl0wQOEUFAMiScc7P0UVK3e8rhSzQ-wy_hsHh4PGdObjUGo_vs3x53ISIh43LftyaHi9b70zEL6F2Pd63wwav_LcPe48XMWb008Qctu4SnTWmT-7qT0do9bD4mD-Vy7fH5_lsWXYU2FBaS5mUoME2YG1FGgO1BsErWQvQaspIla8mFamE41oaUwvJlDYWHFWsZiN0e-zdmWRN38Q83qb1LrZbEw9rwkAzJTN2c8S6NIT4b1OpmFCK_QDe5VuW</recordid><startdate>20000301</startdate><enddate>20000301</enddate><creator>Verdinelli, Isabella</creator><general>Biometrika Trust</general><general>Oxford University Press</general><scope>IQODW</scope></search><sort><creationdate>20000301</creationdate><title>A Note on Bayesian Design for the Normal Linear Model with Unknown Error Variance</title><author>Verdinelli, Isabella</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-j203t-cc2366080cf0ccb1fa0d8054b6d5087931bbbbd1b1b5e486aad56378ac0e273d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2000</creationdate><topic>Bayesian theories</topic><topic>Covariance matrices</topic><topic>Distribution theory</topic><topic>Exact sciences and technology</topic><topic>Expected utility</topic><topic>Experiment design</topic><topic>Linear models</topic><topic>Linear regression</topic><topic>Mathematics</topic><topic>Matrices</topic><topic>Miscellanea</topic><topic>Parametric models</topic><topic>Probability and statistics</topic><topic>Regression analysis</topic><topic>Sciences and techniques of general use</topic><topic>Statistical variance</topic><topic>Statistics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Verdinelli, Isabella</creatorcontrib><collection>Pascal-Francis</collection><jtitle>Biometrika</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Verdinelli, Isabella</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A Note on Bayesian Design for the Normal Linear Model with Unknown Error Variance</atitle><jtitle>Biometrika</jtitle><date>2000-03-01</date><risdate>2000</risdate><volume>87</volume><issue>1</issue><spage>222</spage><epage>227</epage><pages>222-227</pages><issn>0006-3444</issn><eissn>1464-3510</eissn><coden>BIOKAX</coden><abstract>The Bayesian theory of optimal experimental design for the normal linear model has been developed under the assumption of known variance. The insensitivity of specific design criteria to prior assumptions on the variance distribution has been demonstrated in special cases, but a general result showing the way in which Bayesian optimal designs are affected by prior information on the variance is lacking. This note proves that Bayesian designs are insensitive to information about the variance in a more general way than previously thought.</abstract><cop>Oxford</cop><pub>Biometrika Trust</pub><doi>10.1093/biomet/87.1.222</doi><tpages>6</tpages></addata></record> |
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| ispartof | Biometrika, 2000-03, Vol.87 (1), p.222-227 |
| issn | 0006-3444 1464-3510 |
| language | eng |
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| source | JSTOR; Oxford Journals |
| subjects | Bayesian theories Covariance matrices Distribution theory Exact sciences and technology Expected utility Experiment design Linear models Linear regression Mathematics Matrices Miscellanea Parametric models Probability and statistics Regression analysis Sciences and techniques of general use Statistical variance Statistics |
| title | A Note on Bayesian Design for the Normal Linear Model with Unknown Error Variance |
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