Monte Carlo Exact Conditional Tests for Log-Linear and Logistic Models
The form of the exact conditional distribution of a sufficient statistic for the interest parameters, given a sufficient statistic for the nuisance parameters, is derived for a generalized linear model with canonical link. General results for log-linear and logistic models are given. A Gibbs samplin...
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| Veröffentlicht in: | Journal of the Royal Statistical Society. Series B, Methodological Methodological, 1996, Vol.58 (2), p.445-453 |
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| container_end_page | 453 |
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| container_issue | 2 |
| container_start_page | 445 |
| container_title | Journal of the Royal Statistical Society. Series B, Methodological |
| container_volume | 58 |
| creator | Forster, Jonathan J. McDonald, John W. Peter W. F. Smith |
| description | The form of the exact conditional distribution of a sufficient statistic for the interest parameters, given a sufficient statistic for the nuisance parameters, is derived for a generalized linear model with canonical link. General results for log-linear and logistic models are given. A Gibbs sampling approach for generating from the conditional distribution is proposed, which enables Monte Carlo exact conditional tests to be performed. Examples include tests for goodness of fit of the all-two-way interaction model for a 28-table and of a simple logistic model. Tests against non-saturated alternatives are also considered. |
| doi_str_mv | 10.1111/j.2517-6161.1996.tb02092.x |
| format | Article |
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F. Smith</creatorcontrib><title>Monte Carlo Exact Conditional Tests for Log-Linear and Logistic Models</title><title>Journal of the Royal Statistical Society. Series B, Methodological</title><description>The form of the exact conditional distribution of a sufficient statistic for the interest parameters, given a sufficient statistic for the nuisance parameters, is derived for a generalized linear model with canonical link. General results for log-linear and logistic models are given. A Gibbs sampling approach for generating from the conditional distribution is proposed, which enables Monte Carlo exact conditional tests to be performed. Examples include tests for goodness of fit of the all-two-way interaction model for a 28-table and of a simple logistic model. Tests against non-saturated alternatives are also considered.</description><subject>estimated p‐value</subject><subject>Exact sciences and technology</subject><subject>Generalized linear model</subject><subject>gibbs sampler</subject><subject>Goodness of fit</subject><subject>Inference</subject><subject>Linear inference, regression</subject><subject>Logistic regression</subject><subject>Logistics</subject><subject>log‐linear model</subject><subject>Mathematics</subject><subject>monte carlo exact conditional test</subject><subject>P values</subject><subject>Parametric models</subject><subject>Probability and statistics</subject><subject>Regression analysis</subject><subject>Sampling distributions</subject><subject>Sampling theory, sample surveys</subject><subject>Sciences and techniques of general use</subject><subject>Statistical analysis</subject><subject>Statistical methods</subject><subject>Statistical models</subject><subject>Statistics</subject><issn>0035-9246</issn><issn>1369-7412</issn><issn>2517-6161</issn><issn>1467-9868</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>1996</creationdate><recordtype>article</recordtype><recordid>eNqVkMtKxDAUhoMoOF7ewEURcdeaS5M27sbiDUYEL-twJk0kpTZj0sHx7W3tMHuzSQ7nz3cOH0LnBGdkOFdNRjkpUkEEyYiUIuuXmGJJs80emu1a-2iGMeOppLk4REcxNhhjwnI2Q3dPvutNUkFofXK7Ad0nle9q1zvfQZu8mdjHxPqQLPxHunCdgZBAV4-li73TyZOvTRtP0IGFNprT7X2M3u9u36qHdPF8_1jNF6lmjNKU85oao3HOmSypBAkASyIkEWwJhBhpLYOlLYmxMq-pkILZQjJjh3VxoS07RpcTdxX813pYTn26qE3bQmf8OipWlrzkXAzB6ymog48xGKtWwX1C-FEEq1GdatToR41-1KhObdWpzfD5YjsFoobWBui0izsCw7woOB9i8yn27Vrz848B6uX19ebvPTDOJkYTex92DMpyLsuC_QKDIYxH</recordid><startdate>1996</startdate><enddate>1996</enddate><creator>Forster, Jonathan J.</creator><creator>McDonald, John W.</creator><creator>Peter W. 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Smith</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3322-55d2eec04539829a9aaab169163ba11e9ff3abf81ef94d26963f793ef00107cf3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>1996</creationdate><topic>estimated p‐value</topic><topic>Exact sciences and technology</topic><topic>Generalized linear model</topic><topic>gibbs sampler</topic><topic>Goodness of fit</topic><topic>Inference</topic><topic>Linear inference, regression</topic><topic>Logistic regression</topic><topic>Logistics</topic><topic>log‐linear model</topic><topic>Mathematics</topic><topic>monte carlo exact conditional test</topic><topic>P values</topic><topic>Parametric models</topic><topic>Probability and statistics</topic><topic>Regression analysis</topic><topic>Sampling distributions</topic><topic>Sampling theory, sample surveys</topic><topic>Sciences and techniques of general use</topic><topic>Statistical analysis</topic><topic>Statistical methods</topic><topic>Statistical models</topic><topic>Statistics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Forster, Jonathan J.</creatorcontrib><creatorcontrib>McDonald, John W.</creatorcontrib><creatorcontrib>Peter W. F. Smith</creatorcontrib><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>International Bibliography of the Social Sciences</collection><collection>International Bibliography of the Social Sciences</collection><jtitle>Journal of the Royal Statistical Society. Series B, Methodological</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Forster, Jonathan J.</au><au>McDonald, John W.</au><au>Peter W. F. Smith</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Monte Carlo Exact Conditional Tests for Log-Linear and Logistic Models</atitle><jtitle>Journal of the Royal Statistical Society. Series B, Methodological</jtitle><date>1996</date><risdate>1996</risdate><volume>58</volume><issue>2</issue><spage>445</spage><epage>453</epage><pages>445-453</pages><issn>0035-9246</issn><issn>1369-7412</issn><eissn>2517-6161</eissn><eissn>1467-9868</eissn><coden>JSTBAJ</coden><abstract>The form of the exact conditional distribution of a sufficient statistic for the interest parameters, given a sufficient statistic for the nuisance parameters, is derived for a generalized linear model with canonical link. General results for log-linear and logistic models are given. A Gibbs sampling approach for generating from the conditional distribution is proposed, which enables Monte Carlo exact conditional tests to be performed. Examples include tests for goodness of fit of the all-two-way interaction model for a 28-table and of a simple logistic model. Tests against non-saturated alternatives are also considered.</abstract><cop>London</cop><pub>Blackwell Publishers</pub><doi>10.1111/j.2517-6161.1996.tb02092.x</doi><tpages>9</tpages></addata></record> |
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| identifier | ISSN: 0035-9246 |
| ispartof | Journal of the Royal Statistical Society. Series B, Methodological, 1996, Vol.58 (2), p.445-453 |
| issn | 0035-9246 1369-7412 2517-6161 1467-9868 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_38858556 |
| source | JSTOR Mathematics & Statistics; Jstor Complete Legacy; Oxford University Press Journals All Titles (1996-Current) |
| subjects | estimated p‐value Exact sciences and technology Generalized linear model gibbs sampler Goodness of fit Inference Linear inference, regression Logistic regression Logistics log‐linear model Mathematics monte carlo exact conditional test P values Parametric models Probability and statistics Regression analysis Sampling distributions Sampling theory, sample surveys Sciences and techniques of general use Statistical analysis Statistical methods Statistical models Statistics |
| title | Monte Carlo Exact Conditional Tests for Log-Linear and Logistic Models |
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