<?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>0012-7353</journal-id>
<journal-title><![CDATA[DYNA]]></journal-title>
<abbrev-journal-title><![CDATA[Dyna rev.fac.nac.minas]]></abbrev-journal-title>
<issn>0012-7353</issn>
<publisher>
<publisher-name><![CDATA[Universidad Nacional de Colombia]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0012-73532019000400249</article-id>
<article-id pub-id-type="doi">10.15446/dyna.v86n211.80261</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Missing data in multiple correspondence analysis under the available data principle of the NIPALS algorithm]]></article-title>
<article-title xml:lang="es"><![CDATA[Datos faltantes en análisis de correspondencias múltiples bajo el principio de datos disponibles del algoritmo NIPALS]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Ochoa-Muñoz]]></surname>
<given-names><![CDATA[Andrés Felipe]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[González-Rojas]]></surname>
<given-names><![CDATA[Víctor Manuel]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Pardo]]></surname>
<given-names><![CDATA[Campo Elías]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad del Valle Facultad de Ingenierías Escuela de Estadística]]></institution>
<addr-line><![CDATA[Cali ]]></addr-line>
<country>Colombia</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidad Nacional Departamento de Estadística Facultad de Ciencias]]></institution>
<addr-line><![CDATA[Bogotá ]]></addr-line>
<country>Colombia</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>12</month>
<year>2019</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>12</month>
<year>2019</year>
</pub-date>
<volume>86</volume>
<numero>211</numero>
<fpage>249</fpage>
<lpage>257</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_arttext&amp;pid=S0012-73532019000400249&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_abstract&amp;pid=S0012-73532019000400249&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.co/scielo.php?script=sci_pdf&amp;pid=S0012-73532019000400249&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Multiple correspondence analysis (MCA) in the presence of missing data is usually performed by removing the records that have missing or not available (NA) data; sometimes, an entire row or column of a data matrix is removed, which is not ideal because relevant information on an individual or variable of the study is lost. In some cases, it is assumed that the missing data are a category of the qualitative variable, resulting in a greater variance dispersion in the new axes. Possible solutions to this problem can be the imputation of the missing data or using an algorithm suited to the presence of this type of data. This work is focused on performing the MCA method in the presence of missing data, without using imputation techniques, by using the available data principle of the nonlinear estimation by iterative partial least squares (NIPALS) algorithm [25].]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen El Análisis de Correspondencias Múltiples (ACM) en presencia de datos faltantes usualmente se trabaja eliminando los registros en donde exista el dato faltante o no disponible (NA), algunas veces se elimina toda la fila o toda la columna de la matriz de datos, lo cual no es adecuado ya que al realizarlo se pierde información relevante sobre algún individuo o variable del estudio. En algunos otros casos, se asume que el dato faltante es una categoría de la variable cualitativa, trayendo como consecuencia mayor dispersión de varianza en los nuevos ejes. Una solución para esta situación puede ser la imputación del dato faltante o utilizar un algoritmo que permita trabajar con la presencia de éste tipo de datos. Este trabajo se centra en realizar el método ACM en presencia de datos faltantes sin acudir a técnicas de imputación, para esto se utiliza el principio de datos disponibles del algoritmo NIPALS [25].]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[multiple correspondence analysis]]></kwd>
<kwd lng="en"><![CDATA[missing data]]></kwd>
<kwd lng="en"><![CDATA[NIPALS]]></kwd>
<kwd lng="en"><![CDATA[available data principle]]></kwd>
<kwd lng="es"><![CDATA[análisis de correspondencias múltiples]]></kwd>
<kwd lng="es"><![CDATA[datos faltantes]]></kwd>
<kwd lng="es"><![CDATA[NIPALS]]></kwd>
<kwd lng="es"><![CDATA[principio de datos disponibles]]></kwd>
</kwd-group>
</article-meta>
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