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<title>Mathematics and Statistics Faculty Publications</title>
<copyright>Copyright (c) 2013 Georgia State University All rights reserved.</copyright>
<link>http://digitalarchive.gsu.edu/math_facpub</link>
<description>Recent documents in Mathematics and Statistics Faculty Publications</description>
<language>en-us</language>
<lastBuildDate>Fri, 08 Feb 2013 06:30:15 PST</lastBuildDate>
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<title>Comparing Distribution Functions via Empirical Likelihood</title>
<link>http://digitalarchive.gsu.edu/math_facpub/3</link>
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<pubDate>Thu, 08 Oct 2009 10:03:48 PDT</pubDate>
<description>
	<![CDATA[
	<p>This paper develops empirical likelihood based simultaneous confidence bands for differences and ratios of two distribution functions from independent samples of right-censored survival data. The proposed confidence bands provide a flexible way of comparing treatments in biomedical settings, and bring empirical likelihood methods to bear on important target functions for which only Wald-type confidence bands have been available in the literature. The approach is illustrated with a real data example.</p>

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<author>Yichuan Zhao et al.</author>


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<title>A Note on Empirical Likelihood Inference of Residual Life Regression</title>
<link>http://digitalarchive.gsu.edu/math_facpub/2</link>
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<pubDate>Thu, 08 Oct 2009 09:55:03 PDT</pubDate>
<description>
	<![CDATA[
	<p>Mean residual life function, or life expectancy, is an important function to characterize distribution of residual life. The proportional mean residual life model by Oakes and Dasu (1990) is a regression tool to study the association between life expectancy and its associated covariates. Although semiparametric inference procedures have been proposed in the literature, the accuracy of such procedures may be low when the censoring proportion is relatively large. In this paper, the semiparametric inference procedures are studied with an empirical likelihood ratio method. An empirical likelihood confidence region is constructed for the regression parameters. The proposed method is further compared with the normal approximation based method through a simulation study.</p>

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<author>Yichuan Zhao et al.</author>


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