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    <title>topic Re: Selecting smoothing parameters in Discussions</title>
    <link>https://community.jmp.com/t5/Discussions/Selecting-smoothing-parameters/m-p/320686#M57126</link>
    <description>&lt;P&gt;Thanks much Kevin. That paper is obviously pretty technical, but I'm going to see if I can pick some of the more intuitive ideas from it and move on from there as a starting point&lt;/P&gt;</description>
    <pubDate>Tue, 13 Oct 2020 14:27:36 GMT</pubDate>
    <dc:creator>john_madden</dc:creator>
    <dc:date>2020-10-13T14:27:36Z</dc:date>
    <item>
      <title>Selecting smoothing parameters</title>
      <link>https://community.jmp.com/t5/Discussions/Selecting-smoothing-parameters/m-p/320462#M57099</link>
      <description>&lt;P&gt;I have a large number of datasets where the abscissa is date/time and the ordinate is some response variable. I want to show variation over time as a smooth curve. I generally adjust the choice of fit type and the fit parameters (e.g. lambda) interactively until I get a smoothed curve that subjectively seems &lt;EM&gt;informative&lt;/EM&gt;&amp;nbsp;or a&amp;nbsp;&lt;EM&gt;good fit&lt;/EM&gt; to me.&lt;/P&gt;&lt;P&gt;But it's wholly subjective. Have statisticians thought through the question of whether there are &lt;EM&gt;objective&lt;/EM&gt; ways of determining an optimum degree of smoothing?&lt;/P&gt;&lt;P&gt;I imagine &lt;EM&gt;optimum&lt;/EM&gt; could be defined in a myriad of different ways, so maybe this isnt so easy to answer. If there is literature on this, I'd be grateful for some pointers.&lt;/P&gt;&lt;DIV&gt;&amp;nbsp;&lt;/DIV&gt;&lt;DIV&gt;&amp;nbsp;&lt;/DIV&gt;</description>
      <pubDate>Sat, 10 Jun 2023 20:40:07 GMT</pubDate>
      <guid>https://community.jmp.com/t5/Discussions/Selecting-smoothing-parameters/m-p/320462#M57099</guid>
      <dc:creator>john_madden</dc:creator>
      <dc:date>2023-06-10T20:40:07Z</dc:date>
    </item>
    <item>
      <title>Re: Selecting smoothing parameters</title>
      <link>https://community.jmp.com/t5/Discussions/Selecting-smoothing-parameters/m-p/320506#M57104</link>
      <description>&lt;P&gt;Hi, john_madden!&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;As you imagine, this isn't such an easy question to answer.&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;This might not be the best paper with which for you to start, but Grace Wahba has serious spline chops!&amp;nbsp; She wrote the book on it!!&lt;/P&gt;&lt;P&gt;&amp;nbsp;&lt;/P&gt;&lt;P&gt;Good luck.&lt;/P&gt;</description>
      <pubDate>Mon, 12 Oct 2020 22:35:31 GMT</pubDate>
      <guid>https://community.jmp.com/t5/Discussions/Selecting-smoothing-parameters/m-p/320506#M57104</guid>
      <dc:creator>Kevin_Anderson</dc:creator>
      <dc:date>2020-10-12T22:35:31Z</dc:date>
    </item>
    <item>
      <title>Re: Selecting smoothing parameters</title>
      <link>https://community.jmp.com/t5/Discussions/Selecting-smoothing-parameters/m-p/320686#M57126</link>
      <description>&lt;P&gt;Thanks much Kevin. That paper is obviously pretty technical, but I'm going to see if I can pick some of the more intuitive ideas from it and move on from there as a starting point&lt;/P&gt;</description>
      <pubDate>Tue, 13 Oct 2020 14:27:36 GMT</pubDate>
      <guid>https://community.jmp.com/t5/Discussions/Selecting-smoothing-parameters/m-p/320686#M57126</guid>
      <dc:creator>john_madden</dc:creator>
      <dc:date>2020-10-13T14:27:36Z</dc:date>
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