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    <title>topic Re: Cauchy Distribution in SAS/IML Software and Matrix Computations</title>
    <link>https://communities.sas.com/t5/SAS-IML-Software-and-Matrix/Cauchy-Distribution/m-p/145437#M1232</link>
    <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;I've never done this, but here are some ideas that might help:&lt;/P&gt;&lt;P&gt;1) For a graphical test, use the quantile-quantile plot, as described in this article &lt;A href="http://blogs.sas.com/content/iml/2011/10/28/modeling-the-distribution-of-data-create-a-qq-plot/" title="http://blogs.sas.com/content/iml/2011/10/28/modeling-the-distribution-of-data-create-a-qq-plot/"&gt; Modeling the distribution of data? Create a Q-Q plot - The DO Loop&lt;/A&gt; &lt;/P&gt;&lt;P&gt;2) Transform your data by the inverse CDF and then test whether the result is uniform. &lt;/P&gt;&lt;P&gt;3) You can use PROC NLMIXED to use MLE as described in this paper for the t-distribution: &lt;A href="http://www2.sas.com/proceedings/forum2007/181-2007.pdf" title="http://www2.sas.com/proceedings/forum2007/181-2007.pdf"&gt;http://www2.sas.com/proceedings/forum2007/181-2007.pdf&lt;/A&gt; &lt;/P&gt;&lt;P&gt;The Cauchy MLE is easy find online.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Of course, if the normal variables are independent, then no need to test, since this is the definition of the Cauchy distribution.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Regarding the question "if this does not fit, what procedure can I use to evaluate which distribution...,"&amp;nbsp; I recommend that you look at the SEVERITY procedure in SAS/ETS software.&amp;nbsp; Here is an example: &lt;A href="http://support.sas.com/documentation/cdl/en/etsug/67525/HTML/default/viewer.htm#etsug_severity_gettingstarted01.htm" title="http://support.sas.com/documentation/cdl/en/etsug/67525/HTML/default/viewer.htm#etsug_severity_gettingstarted01.htm"&gt;SAS/ETS(R) 13.2 User's Guide&lt;/A&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Good luck.&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
    <pubDate>Mon, 15 Sep 2014 19:56:28 GMT</pubDate>
    <dc:creator>Rick_SAS</dc:creator>
    <dc:date>2014-09-15T19:56:28Z</dc:date>
    <item>
      <title>Cauchy Distribution</title>
      <link>https://communities.sas.com/t5/SAS-IML-Software-and-Matrix/Cauchy-Distribution/m-p/145436#M1231</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;Good afternoon everyone, &lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;I need to test the distribution of the ratio of two normal variable. I believe that this variable follows the Cauchy distribution. Then I would like to know which procedure can test this hypothesis. And, if this variable does not fit in the distribution of Cauchy, What procedure that I can use for evaluate which distribution the variable follows?&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Best regards.&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Mon, 15 Sep 2014 19:12:56 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-IML-Software-and-Matrix/Cauchy-Distribution/m-p/145436#M1231</guid>
      <dc:creator>bonfa</dc:creator>
      <dc:date>2014-09-15T19:12:56Z</dc:date>
    </item>
    <item>
      <title>Re: Cauchy Distribution</title>
      <link>https://communities.sas.com/t5/SAS-IML-Software-and-Matrix/Cauchy-Distribution/m-p/145437#M1232</link>
      <description>&lt;HTML&gt;&lt;HEAD&gt;&lt;/HEAD&gt;&lt;BODY&gt;&lt;P&gt;I've never done this, but here are some ideas that might help:&lt;/P&gt;&lt;P&gt;1) For a graphical test, use the quantile-quantile plot, as described in this article &lt;A href="http://blogs.sas.com/content/iml/2011/10/28/modeling-the-distribution-of-data-create-a-qq-plot/" title="http://blogs.sas.com/content/iml/2011/10/28/modeling-the-distribution-of-data-create-a-qq-plot/"&gt; Modeling the distribution of data? Create a Q-Q plot - The DO Loop&lt;/A&gt; &lt;/P&gt;&lt;P&gt;2) Transform your data by the inverse CDF and then test whether the result is uniform. &lt;/P&gt;&lt;P&gt;3) You can use PROC NLMIXED to use MLE as described in this paper for the t-distribution: &lt;A href="http://www2.sas.com/proceedings/forum2007/181-2007.pdf" title="http://www2.sas.com/proceedings/forum2007/181-2007.pdf"&gt;http://www2.sas.com/proceedings/forum2007/181-2007.pdf&lt;/A&gt; &lt;/P&gt;&lt;P&gt;The Cauchy MLE is easy find online.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Of course, if the normal variables are independent, then no need to test, since this is the definition of the Cauchy distribution.&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Regarding the question "if this does not fit, what procedure can I use to evaluate which distribution...,"&amp;nbsp; I recommend that you look at the SEVERITY procedure in SAS/ETS software.&amp;nbsp; Here is an example: &lt;A href="http://support.sas.com/documentation/cdl/en/etsug/67525/HTML/default/viewer.htm#etsug_severity_gettingstarted01.htm" title="http://support.sas.com/documentation/cdl/en/etsug/67525/HTML/default/viewer.htm#etsug_severity_gettingstarted01.htm"&gt;SAS/ETS(R) 13.2 User's Guide&lt;/A&gt;&lt;/P&gt;&lt;P&gt;&lt;/P&gt;&lt;P&gt;Good luck.&lt;/P&gt;&lt;/BODY&gt;&lt;/HTML&gt;</description>
      <pubDate>Mon, 15 Sep 2014 19:56:28 GMT</pubDate>
      <guid>https://communities.sas.com/t5/SAS-IML-Software-and-Matrix/Cauchy-Distribution/m-p/145437#M1232</guid>
      <dc:creator>Rick_SAS</dc:creator>
      <dc:date>2014-09-15T19:56:28Z</dc:date>
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