Effect of regression to the mean in multivariate distributions
Estimating treatment effects in the presence of regression to the mean is a problem arising in truncated distributions that is being recognized with increasing interest in recent literature. As noted in a previous communication by the authors (1991), any extraneous source of variability such as with...
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| Veröffentlicht in: | Communications in statistics. Theory and methods 1992-01, Vol.21 (2), p.333-350 |
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| container_end_page | 350 |
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| container_start_page | 333 |
| container_title | Communications in statistics. Theory and methods |
| container_volume | 21 |
| creator | George, Varghese T. Johnson, William D. |
| description | Estimating treatment effects in the presence of regression to the mean is a problem arising in truncated distributions that is being recognized with increasing interest in recent literature. As noted in a previous communication by the authors (1991), any extraneous source of variability such as within-subject variability and measurement errors can contribute to the magnitude of regression toward the mean. The main focus of this paper is consideration of a model for estimating treatment effects when truncation and regression to the mean occur on more than one random variable. This situation occurs often in investigations where subjects are selected for study because measurements on two characteristics of interest both exceed specified values. |
| doi_str_mv | 10.1080/03610929208830782 |
| format | Article |
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As noted in a previous communication by the authors (1991), any extraneous source of variability such as within-subject variability and measurement errors can contribute to the magnitude of regression toward the mean. The main focus of this paper is consideration of a model for estimating treatment effects when truncation and regression to the mean occur on more than one random variable. This situation occurs often in investigations where subjects are selected for study because measurements on two characteristics of interest both exceed specified values.</description><identifier>ISSN: 0361-0926</identifier><identifier>EISSN: 1532-415X</identifier><identifier>DOI: 10.1080/03610929208830782</identifier><identifier>CODEN: CSTMDC</identifier><language>eng</language><publisher>Philadelphia, PA: Marcel Dekker, Inc</publisher><subject>Bivariate normal distribution ; Exact sciences and technology ; Mathematics ; measurement error ; Multivariate analysis ; Probability and statistics ; regression to the mean ; repeated measurements ; replicate measurements ; Sciences and techniques of general use ; Statistics ; truncation ; within-subject effect</subject><ispartof>Communications in statistics. Theory and methods, 1992-01, Vol.21 (2), p.333-350</ispartof><rights>Copyright Taylor & Francis Group, LLC 1992</rights><rights>1993 INIST-CNRS</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c325t-fd7df3c1236811b90985bac283ccdb0f225a1c5185292af6d5367b72002e939b3</citedby><cites>FETCH-LOGICAL-c325t-fd7df3c1236811b90985bac283ccdb0f225a1c5185292af6d5367b72002e939b3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.tandfonline.com/doi/pdf/10.1080/03610929208830782$$EPDF$$P50$$Ginformaworld$$H</linktopdf><linktohtml>$$Uhttps://www.tandfonline.com/doi/full/10.1080/03610929208830782$$EHTML$$P50$$Ginformaworld$$H</linktohtml><link.rule.ids>314,780,784,4022,27921,27922,27923,59645,60434</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=4416361$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>George, Varghese T.</creatorcontrib><creatorcontrib>Johnson, William D.</creatorcontrib><title>Effect of regression to the mean in multivariate distributions</title><title>Communications in statistics. Theory and methods</title><description>Estimating treatment effects in the presence of regression to the mean is a problem arising in truncated distributions that is being recognized with increasing interest in recent literature. As noted in a previous communication by the authors (1991), any extraneous source of variability such as within-subject variability and measurement errors can contribute to the magnitude of regression toward the mean. The main focus of this paper is consideration of a model for estimating treatment effects when truncation and regression to the mean occur on more than one random variable. This situation occurs often in investigations where subjects are selected for study because measurements on two characteristics of interest both exceed specified values.</description><subject>Bivariate normal distribution</subject><subject>Exact sciences and technology</subject><subject>Mathematics</subject><subject>measurement error</subject><subject>Multivariate analysis</subject><subject>Probability and statistics</subject><subject>regression to the mean</subject><subject>repeated measurements</subject><subject>replicate measurements</subject><subject>Sciences and techniques of general use</subject><subject>Statistics</subject><subject>truncation</subject><subject>within-subject effect</subject><issn>0361-0926</issn><issn>1532-415X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1992</creationdate><recordtype>article</recordtype><recordid>eNp1kMFKxDAQhoMouK4-gLccvFZnkqZNQQRZdlVY8KLgraRpopG2WZKssm9vl6oX8TSH-b5_mJ-Qc4RLBAlXwAuEilUMpORQSnZAZig4y3IUL4dktt9nI1Ack5MY3wFQlJLPyM3SWqMT9ZYG8xpMjM4PNHma3gztjRqoG2i_7ZL7UMGpZGjrYgqu2aYRjKfkyKoumrPvOSfPq-XT4j5bP949LG7XmeZMpMy2ZWu5RsYLidhUUEnRKM0k17ptwDImFGqBUowfKFu0ghdlUzIAZipeNXxOcMrVwccYjK03wfUq7GqEel9A_aeA0bmYnI2KWnU2qEG7-CvmORajM2LXE-YG60OvPn3o2jqpXefDj8P_v_IFJfZs2A</recordid><startdate>19920101</startdate><enddate>19920101</enddate><creator>George, Varghese T.</creator><creator>Johnson, William D.</creator><general>Marcel Dekker, Inc</general><general>Taylor & Francis</general><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope></search><sort><creationdate>19920101</creationdate><title>Effect of regression to the mean in multivariate distributions</title><author>George, Varghese T. ; Johnson, William D.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c325t-fd7df3c1236811b90985bac283ccdb0f225a1c5185292af6d5367b72002e939b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1992</creationdate><topic>Bivariate normal distribution</topic><topic>Exact sciences and technology</topic><topic>Mathematics</topic><topic>measurement error</topic><topic>Multivariate analysis</topic><topic>Probability and statistics</topic><topic>regression to the mean</topic><topic>repeated measurements</topic><topic>replicate measurements</topic><topic>Sciences and techniques of general use</topic><topic>Statistics</topic><topic>truncation</topic><topic>within-subject effect</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>George, Varghese T.</creatorcontrib><creatorcontrib>Johnson, William D.</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><jtitle>Communications in statistics. Theory and methods</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>George, Varghese T.</au><au>Johnson, William D.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Effect of regression to the mean in multivariate distributions</atitle><jtitle>Communications in statistics. Theory and methods</jtitle><date>1992-01-01</date><risdate>1992</risdate><volume>21</volume><issue>2</issue><spage>333</spage><epage>350</epage><pages>333-350</pages><issn>0361-0926</issn><eissn>1532-415X</eissn><coden>CSTMDC</coden><abstract>Estimating treatment effects in the presence of regression to the mean is a problem arising in truncated distributions that is being recognized with increasing interest in recent literature. As noted in a previous communication by the authors (1991), any extraneous source of variability such as within-subject variability and measurement errors can contribute to the magnitude of regression toward the mean. The main focus of this paper is consideration of a model for estimating treatment effects when truncation and regression to the mean occur on more than one random variable. This situation occurs often in investigations where subjects are selected for study because measurements on two characteristics of interest both exceed specified values.</abstract><cop>Philadelphia, PA</cop><pub>Marcel Dekker, Inc</pub><doi>10.1080/03610929208830782</doi><tpages>18</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0361-0926 |
| ispartof | Communications in statistics. Theory and methods, 1992-01, Vol.21 (2), p.333-350 |
| issn | 0361-0926 1532-415X |
| language | eng |
| recordid | cdi_crossref_primary_10_1080_03610929208830782 |
| source | Taylor & Francis:Master (3349 titles) |
| subjects | Bivariate normal distribution Exact sciences and technology Mathematics measurement error Multivariate analysis Probability and statistics regression to the mean repeated measurements replicate measurements Sciences and techniques of general use Statistics truncation within-subject effect |
| title | Effect of regression to the mean in multivariate distributions |
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