<?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>1405-7743</journal-id>
<journal-title><![CDATA[Ingeniería, investigación y tecnología]]></journal-title>
<abbrev-journal-title><![CDATA[Ing. invest. y tecnol.]]></abbrev-journal-title>
<issn>1405-7743</issn>
<publisher>
<publisher-name><![CDATA[Universidad Nacional Autónoma de México, Facultad de Ingeniería]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S1405-77432016000300383</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Comparing the Intercept Mixture Model with the Slack-Variable Mixture Model]]></article-title>
<article-title xml:lang="es"><![CDATA[Comparación del modelo de mezclas con intercepto con el modelo de mezclas de variable de holgura]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Cruz-Salgado]]></surname>
<given-names><![CDATA[Javier]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Politécnica del Bicentenario  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>México</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>09</month>
<year>2016</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>09</month>
<year>2016</year>
</pub-date>
<volume>17</volume>
<numero>3</numero>
<fpage>383</fpage>
<lpage>393</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S1405-77432016000300383&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S1405-77432016000300383&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S1405-77432016000300383&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract: Mixture experiments are experiments performed using ingredients whose proportions are restricted. This restriction may result in extremely small range in terms of the mixtures, causing difficulties in model fitting arising from ill-conditioning. The choice of model form is a very important factor in the numerical stability of the information matrix. In this paper, the intercept model is compared against the slack-variable model for mixture experiments. We analyzed if it matter which component is replaced for the constant term in the intercept model, in the sense on numerical stability. We also show by numerical examples that the Correlation Criterion, presented in Kang et al. (2015), does not work for the intercept model. Next, as suggested in the literature, we use linear transformation to alleviate the numerical instability. In addition, we try four transformation methods and choose the best one for the intercept model and the slack-variable model. Finally, we compare the intercept model with the slack-variable model based mainly on the prediction accuracy and numerical stability.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen: Los diseños de experimentos para mezclas son diseños que se llevan a cabo usando ingredientes cuyas proporciones están sujetas a restricciones. Dichas restricciones pueden dar como resultado un rango extremadamente pequeño en términos de las mezclas, causando dificultades en el ajuste del modelo debido a problemas de coli- nealidad. La elección de la forma del modelo es un factor importante en la estabilidad numérica de la matriz de información. En este artículo, se compara el modelo intercepto contra el modelo de variable de holgura en experimentos para mezclas. Se analizó la importancia de cuál componente se remplaza por el término constante en el modelo intercepto, en el sentido de estabilidad numérica. Asimismo, se muestra mediante ejemplos numéricos que el Criterio de Correlación, presentado por Kang et al. (2015), no funciona para el modelo intercepto. Después, como se sugiere en la literatura, se emplearon transformaciones numéricas para mejorar la inestabilidad numérica. Adicionalmente, se probaron cuatro métodos de transformación y se seleccionó el mejor, tanto para el modelo intercepto como para el modelo de variable de holgura. Finalmente, se comparó el modelo intercepto contra el modelo de variable de holgura basado principalmente en la precisión de predicción y la estabilidad numérica.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[mixture experiments]]></kwd>
<kwd lng="en"><![CDATA[intercept model]]></kwd>
<kwd lng="en"><![CDATA[slack-variable model]]></kwd>
<kwd lng="en"><![CDATA[variable transformation]]></kwd>
<kwd lng="en"><![CDATA[condition number]]></kwd>
<kwd lng="en"><![CDATA[variance inflation factor]]></kwd>
<kwd lng="es"><![CDATA[diseño de experimentos para mezclas]]></kwd>
<kwd lng="es"><![CDATA[modelo intercepto]]></kwd>
<kwd lng="es"><![CDATA[modelo de variable de holgura]]></kwd>
<kwd lng="es"><![CDATA[transformación de variables]]></kwd>
<kwd lng="es"><![CDATA[número condicional]]></kwd>
<kwd lng="es"><![CDATA[factor de inflación de la varianza]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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