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	<title>Comments on: Statistical Principles?</title>
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	<link>http://normaldeviate.wordpress.com/2012/07/28/statistical-principles/</link>
	<description>Thoughts on Statistics and Machine Learning</description>
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	<item>
		<title>By: TO CONDITION, OR NOT TO CONDITION, THAT IS THE QUESTION &#171; Normal Deviate</title>
		<link>http://normaldeviate.wordpress.com/2012/07/28/statistical-principles/comment-page-1/#comment-5016</link>
		<dc:creator><![CDATA[TO CONDITION, OR NOT TO CONDITION, THAT IS THE QUESTION &#171; Normal Deviate]]></dc:creator>
		<pubDate>Sun, 06 Jan 2013 16:29:30 +0000</pubDate>
		<guid isPermaLink="false">http://normaldeviate.wordpress.com/?p=161#comment-5016</guid>
		<description><![CDATA[[...] on. Let me review a simplified version of Larry Brown&#8217;s (1990) example that I discussed here. You observe  [...]]]></description>
		<content:encoded><![CDATA[<p>[...] on. Let me review a simplified version of Larry Brown&#8217;s (1990) example that I discussed here. You observe  [...]</p>
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	<item>
		<title>By: normaldeviate</title>
		<link>http://normaldeviate.wordpress.com/2012/07/28/statistical-principles/comment-page-1/#comment-1628</link>
		<dc:creator><![CDATA[normaldeviate]]></dc:creator>
		<pubDate>Sun, 30 Sep 2012 21:59:16 +0000</pubDate>
		<guid isPermaLink="false">http://normaldeviate.wordpress.com/?p=161#comment-1628</guid>
		<description><![CDATA[Thanks]]></description>
		<content:encoded><![CDATA[<p>Thanks</p>
]]></content:encoded>
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	<item>
		<title>By: Paulo.</title>
		<link>http://normaldeviate.wordpress.com/2012/07/28/statistical-principles/comment-page-1/#comment-1627</link>
		<dc:creator><![CDATA[Paulo.]]></dc:creator>
		<pubDate>Sun, 30 Sep 2012 21:52:22 +0000</pubDate>
		<guid isPermaLink="false">http://normaldeviate.wordpress.com/?p=161#comment-1627</guid>
		<description><![CDATA[Here: http://www.ime.usp.br/~pmarques/papers/redux.pdf]]></description>
		<content:encoded><![CDATA[<p>Here: <a href="http://www.ime.usp.br/~pmarques/papers/redux.pdf" rel="nofollow">http://www.ime.usp.br/~pmarques/papers/redux.pdf</a></p>
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	<item>
		<title>By: normaldeviate</title>
		<link>http://normaldeviate.wordpress.com/2012/07/28/statistical-principles/comment-page-1/#comment-1626</link>
		<dc:creator><![CDATA[normaldeviate]]></dc:creator>
		<pubDate>Sun, 30 Sep 2012 21:19:20 +0000</pubDate>
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		<description><![CDATA[Thanks
I was not able to get the paper.
LW]]></description>
		<content:encoded><![CDATA[<p>Thanks<br />
I was not able to get the paper.<br />
LW</p>
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	<item>
		<title>By: Paulo.</title>
		<link>http://normaldeviate.wordpress.com/2012/07/28/statistical-principles/comment-page-1/#comment-1625</link>
		<dc:creator><![CDATA[Paulo.]]></dc:creator>
		<pubDate>Sun, 30 Sep 2012 21:04:03 +0000</pubDate>
		<guid isPermaLink="false">http://normaldeviate.wordpress.com/?p=161#comment-1625</guid>
		<description><![CDATA[Dear Larry,

The way I see it, Birnbaum&#039;s results are about equivalences of realizations of experiments; when expressed with the right set theoretical tools (equivalence relations over a well defined space of realizations), it seems to me that the tiny letters saying &quot;Hey, this theorem only applies to one person (one prior) at a time.&quot; are really there. Please, take a look at our short revision paper

  http://proceedings.aip.org/resource/2/apcpcs/1073/1/96_1

especially Example 3. With two different priors, how would you come with a (necessarily reflexive) equivalence relation over the space of realizations?

Best regards,

Paulo.

P.S. I enjoy reading the blog. Please, keep posting.]]></description>
		<content:encoded><![CDATA[<p>Dear Larry,</p>
<p>The way I see it, Birnbaum&#8217;s results are about equivalences of realizations of experiments; when expressed with the right set theoretical tools (equivalence relations over a well defined space of realizations), it seems to me that the tiny letters saying &#8220;Hey, this theorem only applies to one person (one prior) at a time.&#8221; are really there. Please, take a look at our short revision paper</p>
<p>  <a href="http://proceedings.aip.org/resource/2/apcpcs/1073/1/96_1" rel="nofollow">http://proceedings.aip.org/resource/2/apcpcs/1073/1/96_1</a></p>
<p>especially Example 3. With two different priors, how would you come with a (necessarily reflexive) equivalence relation over the space of realizations?</p>
<p>Best regards,</p>
<p>Paulo.</p>
<p>P.S. I enjoy reading the blog. Please, keep posting.</p>
]]></content:encoded>
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		<title>By: Keith O'Rourke</title>
		<link>http://normaldeviate.wordpress.com/2012/07/28/statistical-principles/comment-page-1/#comment-1063</link>
		<dc:creator><![CDATA[Keith O'Rourke]]></dc:creator>
		<pubDate>Fri, 17 Aug 2012 15:00:32 +0000</pubDate>
		<guid isPermaLink="false">http://normaldeviate.wordpress.com/?p=161#comment-1063</guid>
		<description><![CDATA[Look forward to such examples, but I agree with Brian, I don&#039;t see a problem here with likelihood as being the minimal sufficient statistic but rather how to work with it. It might make it clearer to write down a likelihood for each of the one hundred observations (the full likelihood bieng the multiple of these). Each one is a well defined function of the  100,001 unknown paramaters and if you had 100,001+ of these - what to do would be fairly straight forward. 

This brings me to comment why I believe sufficiency itself is bogus. 

Fisher&#039;s original motivation was to summarize say two studies so that with just the summaries, a combined analysis could be done that was as good as having the raw data from both studies. Likelihood does that for _estimation_ but not for testing the fit of the model. The fit of model checked by the joint raw data might easily lead one to reject the model and the likelihoods under the rejected model will not necessarily be sufficient for the new model. 

And today we can just archive the data for later re-use and hence summaries serve no purpose. (David Cox corrected me on that once saying they are useful for spliting up information for instance into that for estimation and that for testing fit.)]]></description>
		<content:encoded><![CDATA[<p>Look forward to such examples, but I agree with Brian, I don&#8217;t see a problem here with likelihood as being the minimal sufficient statistic but rather how to work with it. It might make it clearer to write down a likelihood for each of the one hundred observations (the full likelihood bieng the multiple of these). Each one is a well defined function of the  100,001 unknown paramaters and if you had 100,001+ of these &#8211; what to do would be fairly straight forward. </p>
<p>This brings me to comment why I believe sufficiency itself is bogus. </p>
<p>Fisher&#8217;s original motivation was to summarize say two studies so that with just the summaries, a combined analysis could be done that was as good as having the raw data from both studies. Likelihood does that for _estimation_ but not for testing the fit of the model. The fit of model checked by the joint raw data might easily lead one to reject the model and the likelihoods under the rejected model will not necessarily be sufficient for the new model. </p>
<p>And today we can just archive the data for later re-use and hence summaries serve no purpose. (David Cox corrected me on that once saying they are useful for spliting up information for instance into that for estimation and that for testing fit.)</p>
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		<title>By: Justin Smith</title>
		<link>http://normaldeviate.wordpress.com/2012/07/28/statistical-principles/comment-page-1/#comment-916</link>
		<dc:creator><![CDATA[Justin Smith]]></dc:creator>
		<pubDate>Thu, 09 Aug 2012 14:42:32 +0000</pubDate>
		<guid isPermaLink="false">http://normaldeviate.wordpress.com/?p=161#comment-916</guid>
		<description><![CDATA[Thanks for this. Clears a lot of stuff up that I was fuzzy on,]]></description>
		<content:encoded><![CDATA[<p>Thanks for this. Clears a lot of stuff up that I was fuzzy on,</p>
]]></content:encoded>
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	<item>
		<title>By: fred</title>
		<link>http://normaldeviate.wordpress.com/2012/07/28/statistical-principles/comment-page-1/#comment-820</link>
		<dc:creator><![CDATA[fred]]></dc:creator>
		<pubDate>Sat, 04 Aug 2012 23:31:55 +0000</pubDate>
		<guid isPermaLink="false">http://normaldeviate.wordpress.com/?p=161#comment-820</guid>
		<description><![CDATA[But *which* Bayes estimator? Estimators are just ways to summarize the posterior - so there are many one could use. Some estimators may end up giving sparse estimates, even when the prior and/or posterior give little (or zero) posterior support to sparseness in the true underlying parameters. 

Just like there&#039;s no &quot;hidden label&quot; in Birnbaum (which is a great point!) there&#039;s nothing in Bayes that says one has to use the posterior mean/median/mode.]]></description>
		<content:encoded><![CDATA[<p>But *which* Bayes estimator? Estimators are just ways to summarize the posterior &#8211; so there are many one could use. Some estimators may end up giving sparse estimates, even when the prior and/or posterior give little (or zero) posterior support to sparseness in the true underlying parameters. </p>
<p>Just like there&#8217;s no &#8220;hidden label&#8221; in Birnbaum (which is a great point!) there&#8217;s nothing in Bayes that says one has to use the posterior mean/median/mode.</p>
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		<title>By: Greg</title>
		<link>http://normaldeviate.wordpress.com/2012/07/28/statistical-principles/comment-page-1/#comment-758</link>
		<dc:creator><![CDATA[Greg]]></dc:creator>
		<pubDate>Thu, 02 Aug 2012 19:10:53 +0000</pubDate>
		<guid isPermaLink="false">http://normaldeviate.wordpress.com/?p=161#comment-758</guid>
		<description><![CDATA[It seems to me that CP and SP are more plausible when formulated as claims about the evidential meaning of experimental outcomes (which is how Birnbaum formulated them) than when they are formulated as claims about what our inferences ought to depend on.  The problem with this formulation from a likelihoodist or Bayesian point of view is that it seems to make LP toothless: a frequentist could accept Birnbaum&#039;s proof without abandoning frequentist methods (as Birnbaum himself did) by denying that a statistical method must respect evidential equivalence.]]></description>
		<content:encoded><![CDATA[<p>It seems to me that CP and SP are more plausible when formulated as claims about the evidential meaning of experimental outcomes (which is how Birnbaum formulated them) than when they are formulated as claims about what our inferences ought to depend on.  The problem with this formulation from a likelihoodist or Bayesian point of view is that it seems to make LP toothless: a frequentist could accept Birnbaum&#8217;s proof without abandoning frequentist methods (as Birnbaum himself did) by denying that a statistical method must respect evidential equivalence.</p>
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	<item>
		<title>By: normaldeviate</title>
		<link>http://normaldeviate.wordpress.com/2012/07/28/statistical-principles/comment-page-1/#comment-710</link>
		<dc:creator><![CDATA[normaldeviate]]></dc:creator>
		<pubDate>Wed, 01 Aug 2012 13:28:24 +0000</pubDate>
		<guid isPermaLink="false">http://normaldeviate.wordpress.com/?p=161#comment-710</guid>
		<description><![CDATA[Fair enough. But there are examples where
(i) the likelihood function contains no information
(ii) yet there exist good estimators.
In fact, I am preparing a post on this right now.
---LW]]></description>
		<content:encoded><![CDATA[<p>Fair enough. But there are examples where<br />
(i) the likelihood function contains no information<br />
(ii) yet there exist good estimators.<br />
In fact, I am preparing a post on this right now.<br />
&#8212;LW</p>
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