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    <title>topic Selection of covariance structures in GLIMMIX in Statistical Procedures</title>
    <link>https://communities.sas.com/t5/Statistical-Procedures/Selection-of-covariance-structures-in-GLIMMIX/m-p/670993#M32063</link>
    <description>&lt;P&gt;Stroup and Claassen (2020) recently published an article titled&amp;nbsp;&lt;EM&gt;&lt;FONT face="helvetica" size="3"&gt;Pseudo-Likelihood or Quadrature? What We Thought We Knew, What We Think We Know, and What We Are Still Trying to Figure Out&amp;nbsp;&lt;/FONT&gt;&lt;/EM&gt;&lt;FONT face="helvetica" size="3"&gt;in the Journal of Agricultural, Biological and Environmental Statistics&amp;nbsp;&lt;A href="http://doi.org/10.1007/s13253-020-00402-6" target="_self"&gt;doi.org/10.1007/s13253-020-00402-6&lt;/A&gt;&amp;nbsp; (paywall, unless you are an ASA member).&amp;nbsp; In the article, they give some very solid reasoning for using the default RSPL method for GLIMMIX for a variety of distributions.&amp;nbsp; Now comes the problem - RSPL does not allow for the calculation of information criteria, as the linearization leads to different pseudo-data for various covariance structures.&lt;/FONT&gt;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;&lt;FONT face="helvetica" size="3"&gt;My question is this - would it make sense to use Gaussian quadrature or Laplace method to select a covariance structure based on (for instance) AICC, and then switch to RSPL for a final analysis?&lt;/FONT&gt;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;&lt;FONT face="helvetica" size="3"&gt;SteveDenham&lt;/FONT&gt;&lt;/P&gt;</description>
    <pubDate>Tue, 21 Jul 2020 13:57:47 GMT</pubDate>
    <dc:creator>SteveDenham</dc:creator>
    <dc:date>2020-07-21T13:57:47Z</dc:date>
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      <title>Selection of covariance structures in GLIMMIX</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/Selection-of-covariance-structures-in-GLIMMIX/m-p/670993#M32063</link>
      <description>&lt;P&gt;Stroup and Claassen (2020) recently published an article titled&amp;nbsp;&lt;EM&gt;&lt;FONT face="helvetica" size="3"&gt;Pseudo-Likelihood or Quadrature? What We Thought We Knew, What We Think We Know, and What We Are Still Trying to Figure Out&amp;nbsp;&lt;/FONT&gt;&lt;/EM&gt;&lt;FONT face="helvetica" size="3"&gt;in the Journal of Agricultural, Biological and Environmental Statistics&amp;nbsp;&lt;A href="http://doi.org/10.1007/s13253-020-00402-6" target="_self"&gt;doi.org/10.1007/s13253-020-00402-6&lt;/A&gt;&amp;nbsp; (paywall, unless you are an ASA member).&amp;nbsp; In the article, they give some very solid reasoning for using the default RSPL method for GLIMMIX for a variety of distributions.&amp;nbsp; Now comes the problem - RSPL does not allow for the calculation of information criteria, as the linearization leads to different pseudo-data for various covariance structures.&lt;/FONT&gt;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;&lt;FONT face="helvetica" size="3"&gt;My question is this - would it make sense to use Gaussian quadrature or Laplace method to select a covariance structure based on (for instance) AICC, and then switch to RSPL for a final analysis?&lt;/FONT&gt;&lt;/P&gt;
&lt;P&gt;&amp;nbsp;&lt;/P&gt;
&lt;P&gt;&lt;FONT face="helvetica" size="3"&gt;SteveDenham&lt;/FONT&gt;&lt;/P&gt;</description>
      <pubDate>Tue, 21 Jul 2020 13:57:47 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/Selection-of-covariance-structures-in-GLIMMIX/m-p/670993#M32063</guid>
      <dc:creator>SteveDenham</dc:creator>
      <dc:date>2020-07-21T13:57:47Z</dc:date>
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