<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>0120-1751</journal-id>
<journal-title><![CDATA[Revista Colombiana de Estadística]]></journal-title>
<abbrev-journal-title><![CDATA[Rev.Colomb.Estad.]]></abbrev-journal-title>
<issn>0120-1751</issn>
<publisher>
<publisher-name><![CDATA[Departamento de Estadística - Universidad Nacional de Colombia.]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0120-17512018000100075</article-id>
<article-id pub-id-type="doi">10.15446/rce.v41n1.63152</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Finite Mixture of Compositional Regression With Gaussian Errors]]></article-title>
<article-title xml:lang="es"><![CDATA[Mixtura finita de una regresión composicional con errores Gaussianos]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Shimizu]]></surname>
<given-names><![CDATA[Taciana]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Louzada]]></surname>
<given-names><![CDATA[Francisco]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Suzuki]]></surname>
<given-names><![CDATA[Adriano]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,University of Sao Paulo  ]]></institution>
<addr-line><![CDATA[Sao Paulo ]]></addr-line>
<country>Brazil</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>06</month>
<year>2018</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>06</month>
<year>2018</year>
</pub-date>
<volume>41</volume>
<numero>1</numero>
<fpage>75</fpage>
<lpage>86</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S0120-17512018000100075&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S0120-17512018000100075&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S0120-17512018000100075&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract In this paper, we consider to evaluate the efficiency of volleyball players according to your performance of attack, block and serve, considering the compositional structure of the data related to the fundaments of this sport. In this way, we consider a nite mixture of regression model to compositional data. The maximum likelihood estimation of this model was obtained via an EM algorithm. A simulation study reveals that the parameters are correctly recovery. In addition, the estimators are asymptotically unbiased. By considering real dataset of Brazilian volleyball competition, we show that the model proposed presents best fit than the usual regression model.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen En este estudio evaluamos la eficiencia de los jugadores de voleibol de acuerdo con su desempeño de ataque, bloqueo y servicio, teniendo en cuenta la estructura composicional de los datos relacionados con los fundamentos de este deporte. Así, consideramos un modelo de regresión de mixtura finita para datos composicionales. La estimación de máxima verosimilitud fue obtenida via un Algoritmo EM. Un estudio de simulación revela que los parámetros son correctamente recuperados. Adicionalmente, los estimadores son asintóticamente insesgados. Considerando dados reales del campeonato de voleyball brasileño nosotros mostramos que el modelo propuesto presenta mejor ajuste que el modelo de regresión usual.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Compositional Data]]></kwd>
<kwd lng="en"><![CDATA[Finite Mixture Regression]]></kwd>
<kwd lng="en"><![CDATA[EM Algorithm]]></kwd>
<kwd lng="es"><![CDATA[algoritmo EM]]></kwd>
<kwd lng="es"><![CDATA[Datos Composicionales]]></kwd>
<kwd lng="es"><![CDATA[mixtura finita]]></kwd>
</kwd-group>
</article-meta>
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