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    <title>topic Re: PROC MCMC Multivariate t Prior in Mixed Model in Statistical Procedures</title>
    <link>https://communities.sas.com/t5/Statistical-Procedures/PROC-MCMC-Multivariate-t-Prior-in-Mixed-Model/m-p/255068#M13457</link>
    <description>&lt;P&gt;&lt;SPAN&gt;I'n not an expert on MCMC, but there are two places in the doc that seem relevant. The first is that&amp;nbsp;the multivariate t distribution is &lt;A href="http://support.sas.com/documentation/cdl/en/statug/68162/HTML/default/viewer.htm#statug_mcmc_details17.htm" target="_self"&gt;not one of the built-in multivariate distributions that are supported by PROC MCMC.&lt;/A&gt;&amp;nbsp; The second is in t&lt;A href="http://support.sas.com/documentation/cdl/en/statug/68162/HTML/default/viewer.htm#statug_mcmc_details06.htm" target="_self"&gt;he section on sampling methods&lt;/A&gt; where it states that you can &lt;A href="http://support.sas.com/documentation/cdl/en/statug/68162/HTML/default/viewer.htm#statug_mcmc_syntax11.htm" target="_self"&gt;specify a t distribution on the PARMS statement multidimensional parameters.&lt;/A&gt;&lt;/SPAN&gt;&lt;/P&gt;</description>
    <pubDate>Mon, 07 Mar 2016 19:28:40 GMT</pubDate>
    <dc:creator>Rick_SAS</dc:creator>
    <dc:date>2016-03-07T19:28:40Z</dc:date>
    <item>
      <title>PROC MCMC Multivariate t Prior in Mixed Model</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/PROC-MCMC-Multivariate-t-Prior-in-Mixed-Model/m-p/255062#M13456</link>
      <description>&lt;P&gt;Hi all,&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;I was wondering if anyone has tried to specify a multivariate t distribution for the fixed effects in a mixed model. I have some code below that I used to fit a model to some simulated data. In this code, I assumed a multivariate normal prior on the beta's. Does anyone know whether or not I can specify a multivariate t? I looked into the GENERAL statement in PROC MCMC where you can specify custom distributions. Does it look like I'll have to use GENERAL?&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;PRE&gt;&lt;CODE class=" language-sas"&gt;proc mcmc data = SimulatedData;
	array theta[2] b0 b1;
		array theta_c[2];
		array mu0[2] (0,0);
		array sigma_c[2,2];
		array sigma_0[2,2] (10000 0 0 10000);
		array s[2,2] (0.02 0 0 20);
	parms theta_c sigma_c {.1 0 0 .1} var_y;
	prior theta_c ~ mvn(mu0, sigma_0);
		prior sigma_c ~ iwish(2, s);
		prior var_y ~ igamma(0.01, scale = 0.01);
	random theta ~ mvn(theta_c, sigma_c) subject = Subject;
	mu = b0 + b1*Time;
	model Response ~ Normal(mu, var = var_y);
run;&lt;/CODE&gt;&lt;/PRE&gt;&lt;P&gt;Thanks.&lt;/P&gt;</description>
      <pubDate>Mon, 07 Mar 2016 19:06:35 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/PROC-MCMC-Multivariate-t-Prior-in-Mixed-Model/m-p/255062#M13456</guid>
      <dc:creator>tbanh</dc:creator>
      <dc:date>2016-03-07T19:06:35Z</dc:date>
    </item>
    <item>
      <title>Re: PROC MCMC Multivariate t Prior in Mixed Model</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/PROC-MCMC-Multivariate-t-Prior-in-Mixed-Model/m-p/255068#M13457</link>
      <description>&lt;P&gt;&lt;SPAN&gt;I'n not an expert on MCMC, but there are two places in the doc that seem relevant. The first is that&amp;nbsp;the multivariate t distribution is &lt;A href="http://support.sas.com/documentation/cdl/en/statug/68162/HTML/default/viewer.htm#statug_mcmc_details17.htm" target="_self"&gt;not one of the built-in multivariate distributions that are supported by PROC MCMC.&lt;/A&gt;&amp;nbsp; The second is in t&lt;A href="http://support.sas.com/documentation/cdl/en/statug/68162/HTML/default/viewer.htm#statug_mcmc_details06.htm" target="_self"&gt;he section on sampling methods&lt;/A&gt; where it states that you can &lt;A href="http://support.sas.com/documentation/cdl/en/statug/68162/HTML/default/viewer.htm#statug_mcmc_syntax11.htm" target="_self"&gt;specify a t distribution on the PARMS statement multidimensional parameters.&lt;/A&gt;&lt;/SPAN&gt;&lt;/P&gt;</description>
      <pubDate>Mon, 07 Mar 2016 19:28:40 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/PROC-MCMC-Multivariate-t-Prior-in-Mixed-Model/m-p/255068#M13457</guid>
      <dc:creator>Rick_SAS</dc:creator>
      <dc:date>2016-03-07T19:28:40Z</dc:date>
    </item>
    <item>
      <title>Re: PROC MCMC Multivariate t Prior in Mixed Model</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/PROC-MCMC-Multivariate-t-Prior-in-Mixed-Model/m-p/255070#M13458</link>
      <description>Yes, multivariate t is not one of the recognized distributions. I tried specifying a t-distribution on the PARMS statement but that did not lead to the goal of specifying a multivariate t on the fixed effects. I am currently trying to use the GENERAL statement to specify a custom multivariate t. Have you had any experience with this?</description>
      <pubDate>Mon, 07 Mar 2016 19:35:22 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/PROC-MCMC-Multivariate-t-Prior-in-Mixed-Model/m-p/255070#M13458</guid>
      <dc:creator>tbanh</dc:creator>
      <dc:date>2016-03-07T19:35:22Z</dc:date>
    </item>
    <item>
      <title>Re: PROC MCMC Multivariate t Prior in Mixed Model</title>
      <link>https://communities.sas.com/t5/Statistical-Procedures/PROC-MCMC-Multivariate-t-Prior-in-Mixed-Model/m-p/255074#M13459</link>
      <description>&lt;P&gt;No, I have no experience with this. However, &lt;A href="http://nd.psychstat.org/_media/research/zhang_2013.pdf" target="_self"&gt;Zhang (2013, p. 16-17) shows how to use generalized error distributions in PROC MCMC &lt;/A&gt;by using the&amp;nbsp;GENERAL statement. Perhaps that will be helpful for you.&lt;/P&gt;</description>
      <pubDate>Mon, 07 Mar 2016 19:49:21 GMT</pubDate>
      <guid>https://communities.sas.com/t5/Statistical-Procedures/PROC-MCMC-Multivariate-t-Prior-in-Mixed-Model/m-p/255074#M13459</guid>
      <dc:creator>Rick_SAS</dc:creator>
      <dc:date>2016-03-07T19:49:21Z</dc:date>
    </item>
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