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		<title>en&gt;Matthiaspaul: Created disambiguation page with various meaning of Aperture value</title>
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		<updated>2012-10-04T01:14:22Z</updated>

		<summary type="html">&lt;p&gt;Created disambiguation page with various meaning of Aperture value&lt;/p&gt;
&lt;p&gt;&lt;b&gt;New page&lt;/b&gt;&lt;/p&gt;&lt;div&gt;&amp;#039;&amp;#039;&amp;#039;Empirical likelihood&amp;#039;&amp;#039;&amp;#039; (EL) is an estimation method in [[statistics]]. Empirical likelihood estimates require few assumptions about the error distribution compared to similar methods like [[maximum likelihood]]. EL can handle data well as long as it is [[independent and identically distributed]] (iid). EL performs well even when the distribution is asymmetric or censored.  EL methods are also useful since they can easily incorporate constraints and prior information. [[Art Owen]] pioneered work in this area with his 1988 paper.&lt;br /&gt;
&lt;br /&gt;
==Estimation procedure==&lt;br /&gt;
EL estimates are calculated by maximizing the empirical [[likelihood function]] subject to constraints based on the [[estimating function]] and the trivial assumption that the probability weights of the likelihood function sum to 1.&amp;lt;ref&amp;gt;Mittelhammer, Judge, and Miller (2000), 292.&amp;lt;/ref&amp;gt; This procedure is represented:&lt;br /&gt;
: &amp;lt;math&amp;gt;&lt;br /&gt;
    \max_{\pi_{i}, \theta} \sum_{i=1}^n \ln \pi_{i} &lt;br /&gt;
  &amp;lt;/math&amp;gt;&lt;br /&gt;
Subject to the constraints&lt;br /&gt;
:&amp;lt;math&amp;gt;&lt;br /&gt;
     s.t. \sum_{i=1}^n \pi_{i} = 1, \sum_{i=1}^n \pi_{i} h(y_{i};\theta) = 0&lt;br /&gt;
 &amp;lt;/math&amp;gt;&amp;lt;ref&amp;gt;Bera, Y. Bilias (2002), 77.&amp;lt;/ref&amp;gt;&lt;br /&gt;
&lt;br /&gt;
The value of the theta parameter can be found by solving the [[Lagrangian]]:&lt;br /&gt;
:&amp;lt;math&amp;gt;&lt;br /&gt;
  \mathcal{L} = \sum_{i=1}^n \pi_{i} + \mu (1- \sum_{i=1}^n \pi_{i})-n\tau&amp;#039; \sum_{i=1}^n \pi_{i} h(y_{i};\theta)&lt;br /&gt;
&amp;lt;/math&amp;gt;&amp;lt;ref&amp;gt;Bera, Y. Bilias (2002), 77.&amp;lt;/ref&amp;gt;&lt;br /&gt;
&lt;br /&gt;
==See also==&lt;br /&gt;
* [[Bootstrapping (statistics)]]&lt;br /&gt;
* [[Jackknife (statistics)]]&lt;br /&gt;
&lt;br /&gt;
==Notes==&lt;br /&gt;
{{reflist}}&lt;br /&gt;
&lt;br /&gt;
==References==&lt;br /&gt;
* {{cite journal |last1=Bera |first1=Anil K. |last2=Bilias |first2=Yannis |year=2002 |title=The MM, ME, ML, EL, EF and GMM approaches to estimation: a synthesis |journal=Journal of Econometrics|issue=1-2,number 107 |pages=51–86}}&lt;br /&gt;
* {{cite book |last1=Mittelhammer |first1=Ron C. |last2=Judge |first2=George G. |last3=Miller |first3=Douglas J. |title=Econometric Foundations |publisher=Cambridge University Press |location=New York |year=2000 |isbn=0521623944}}&lt;br /&gt;
* Owen, Art B. &amp;quot;Empirical likelihood ratio confidence intervals for a single functional.&amp;quot; Biometrika 75.2 (1988): 237-249. jstor&lt;br /&gt;
&lt;br /&gt;
[[Category:Estimation theory]]&lt;br /&gt;
[[Category:Fitting probability distributions]]&lt;/div&gt;</summary>
		<author><name>en&gt;Matthiaspaul</name></author>
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