Miscellanea. 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_title | Biometrika |
| container_volume | 87 |
| creator | Verdinelli, I |
| 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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A note on Bayesian design for the normal linear model with unknown error variance</title><source>JSTOR Mathematics & Statistics</source><source>Jstor Complete Legacy</source><source>Oxford University Press Journals All Titles (1996-Current)</source><creator>Verdinelli, I</creator><creatorcontrib>Verdinelli, I</creatorcontrib><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. 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A note on Bayesian design for the normal linear model with unknown error variance</title><title>Biometrika</title><addtitle>Biometrika</addtitle><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.</description><subject>Bayesian design criteria</subject><subject>Design for prediction</subject><subject>Design optimization</subject><subject>Expected utility</subject><subject>Expected utility function</subject><subject>Factorial experiments</subject><subject>Linear model</subject><subject>Parameter estimation</subject><subject>Utility functions</subject><issn>0006-3444</issn><issn>1464-3510</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2000</creationdate><recordtype>article</recordtype><recordid>eNo9kF1LwzAUhoMoOKfX3gbv2-WjSdvLOXRTpoIoyC4MaXvqurWJJp1z_96MiVcvh_O858CD0CUlMSU5HxWN7aAfZWlMY8bYERrQRCYRF5QcowEhREY8SZJTdOb9aj9KIQfo_aHxJbStNqBjPMbG9oCtwdd6B77RBlchPgyurcP9EsLedbrFbRN4hztbQYu3Tb_EG7M2dmswOBfQb-1CuYRzdFLr1sPFXw7R6-3Ny2QWzZ-md5PxPCqpoDKirM5TXvA6zwRhkAgopUhFltVMVIRXecFKnXGdQiFZoUFokJykrKh5WScZ40N0dbj76ezXBnyvVnbjTHipGKEyTyiXARodoNJZ7x3U6tM1nXY7RYnaO1QHhypLFVXBYWhEh0bje_j5x7VbK5nyVKjZ20KJKV2Q5_mjuue_DEh1Sw</recordid><startdate>200003</startdate><enddate>200003</enddate><creator>Verdinelli, I</creator><general>Oxford University Press</general><general>Oxford Publishing Limited (England)</general><scope>BSCLL</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QO</scope><scope>8FD</scope><scope>FR3</scope><scope>P64</scope></search><sort><creationdate>200003</creationdate><title>Miscellanea. A note on Bayesian design for the normal linear model with unknown error variance</title><author>Verdinelli, I</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c1516-12f973b3f98502e45ec657588f25d03d9b2ca83a7eb62bae5ae63072bf3cf4823</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2000</creationdate><topic>Bayesian design criteria</topic><topic>Design for prediction</topic><topic>Design optimization</topic><topic>Expected utility</topic><topic>Expected utility function</topic><topic>Factorial experiments</topic><topic>Linear model</topic><topic>Parameter estimation</topic><topic>Utility functions</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Verdinelli, I</creatorcontrib><collection>Istex</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><jtitle>Biometrika</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Verdinelli, I</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Miscellanea. A note on Bayesian design for the normal linear model with unknown error variance</atitle><jtitle>Biometrika</jtitle><addtitle>Biometrika</addtitle><date>2000-03</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>Oxford University Press</pub><doi>10.1093/biomet/87.1.222</doi><tpages>6</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0006-3444 |
| ispartof | Biometrika, 2000-03, Vol.87 (1), p.222-227 |
| issn | 0006-3444 1464-3510 |
| language | eng |
| recordid | cdi_proquest_journals_201694136 |
| source | JSTOR Mathematics & Statistics; Jstor Complete Legacy; Oxford University Press Journals All Titles (1996-Current) |
| subjects | Bayesian design criteria Design for prediction Design optimization Expected utility Expected utility function Factorial experiments Linear model Parameter estimation Utility functions |
| title | Miscellanea. A note on Bayesian design for the normal linear model with unknown error variance |
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