<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE ArticleSet PUBLIC "-//NLM//DTD PubMed 2.6//EN" "http://www.ncbi.nlm.nih.gov/corehtml/query/static/PubMed.dtd">
<ArticleSet>
<Article>
<Journal>
				<PublisherName>Iranian Mathematical Society (IMS)</PublisherName>
				<JournalTitle>Bulletin of the Iranian Mathematical Society</JournalTitle>
				<Issn>1017-060X</Issn>
				<Volume>37</Volume>
				<Issue>No. 4</Issue>
				<PubDate PubStatus="epublish">
					<Year>2011</Year>
					<Month>12</Month>
					<Day>15</Day>
				</PubDate>
			</Journal>
<ArticleTitle>Using Kullback-Leibler distance for performance evaluation  of search designs</ArticleTitle><FirstPage>269</FirstPage>
			<LastPage>279</LastPage>
			<Language>en</Language>
<AuthorList>
<Author>
					<FirstName>H. </FirstName>
					<LastName>Talebi</LastName>
					<Affiliation></Affiliation>
				</Author>
<Author>
					<FirstName>N. </FirstName>
					<LastName>Esmailzadeh</LastName>
					<Affiliation></Affiliation>
				</Author>
</AuthorList>
			<History>
				<PubDate PubStatus="received">
					<Year>2010</Year>
					<Month>06</Month>
					<Day>28</Day>
				</PubDate>
			</History>
		<Abstract><![CDATA[This paper considers the search problem, introduced by Srivastava cite{Sr}. This is  a model discrimination problem. In the context of search linear models, discrimination ability  of search designs has been studied by several researchers. Some criteria have been developed to measure this capability, however, they are restricted in a sense of being able to work for searching  only   one possible nonzero effect. In this paper, two criteria are proposed, based on Kullback-Leibler distance. These criteria are able to evaluate the search ability of designs, without any restriction on the number of nonzero effects.]]></Abstract>
		<ObjectList>
			<Object Type="keyword">
			<Param Name="value">Search designs</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">search linear model</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Kullback-Leibler distance</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">model  discrimination</Param>
			</Object>
		</ObjectList>
</Article>
</ArticleSet>