<?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>S1405-77432016000100131</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Ajuste de las distribuciones GVE, LOG y PAG con momentos L de orden mayor]]></article-title>
<article-title xml:lang="en"><![CDATA[Fitting of GEV, GLO and GPA Distributions with Higher-Order L Moments]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Campos-Aranda]]></surname>
<given-names><![CDATA[Daniel Francisco]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,campos_aranda@hotmail.com  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>México</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>03</month>
<year>2016</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>03</month>
<year>2016</year>
</pub-date>
<volume>17</volume>
<numero>1</numero>
<fpage>131</fpage>
<lpage>142</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S1405-77432016000100131&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S1405-77432016000100131&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S1405-77432016000100131&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen: El análisis probabilístico de datos hidrológicos extremos busca estimar predicciones confiables asociadas a altos periodos de retorno. Los momentos LH o de orden mayor, permiten caracterizar de una manera más eficiente la cola derecha de la función de distribución de probabilidades (FDP), al dar más importancia a los grandes valores de los datos. Usando los momentos LH se reduce la influencia indeseable que las magnitudes pequeñas de la muestra pueden tener en la estimación de las predicciones. En este trabajo se describe brevemente la teoría de los momentos L, como base para su generalización, lo cual conduce a los momentos LH, propuestos en 1997 por Wang Q.J. Se citan las ecuaciones que permiten estimar, con los métodos de momentos L y momentos LH, los tres parámetros de ajuste de las FDP: general de valores extremos (GVE), logística generalizada (LOG) y Pareto generalizada (PAG). Se realiza una aplicación numérica a los 21 registros disponibles de crecientes anuales de la Región Hidrológica Núm. 10 (Sinaloa), contrastando sus resultados con base en el error estándar de ajuste (EEA). El análisis de resultados mostró que la distribución PAG conduce a los mejores ajustes. También se observó que los momentos LH son una buena opción para abatir el EEA en las tres distribuciones utilizadas.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract: The probabilistic analysis of extreme hydrological data aims to estimate reliable predictions associated with high return periods. The LH or higher-order moments can characterize in a more efficient way the right tail of the probability distribution function (PDF), by giving more importance to large data values. The undesirable influence of the sample's small quantities is reduced by using the LH moments in the estimation of predictions. This paper briefly describes the theory of L moments, as a basis for its generalization, which leads to the LH moments, proposed in 1997 by Wang Q.J. By means of the L and LH moments methods, the equations that allow the estimation of the three fitting parameters of the PDF: General Extreme Values (GEV), Generalized Logistics (GLO) and Generalized Pareto (GPA) are cited. A numerical application to the 21 available records annual floods of Hydrological Region No. 10 (Sinaloa) is performed, contrasting its results based on the standard error of fit (SEF). The analysis of results showed that the GPA distribution leads to the best fittings. It can be highlighted that LH moments are a good choice to abate the SEF in the three distributions used.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[momentos L]]></kwd>
<kwd lng="es"><![CDATA[momentos LH o de orden mayor]]></kwd>
<kwd lng="es"><![CDATA[distribuciones GVE, LOG y PAG]]></kwd>
<kwd lng="es"><![CDATA[error estándar de ajuste]]></kwd>
<kwd lng="es"><![CDATA[Región Hidrológica Núm.10 (Sinaloa)]]></kwd>
<kwd lng="en"><![CDATA[L moments]]></kwd>
<kwd lng="en"><![CDATA[higher-order L moments]]></kwd>
<kwd lng="en"><![CDATA[LH moments]]></kwd>
<kwd lng="en"><![CDATA[GEV, GLO and GPA distributions]]></kwd>
<kwd lng="en"><![CDATA[standard error of fit]]></kwd>
<kwd lng="en"><![CDATA[Hydrological Region No. 10 (Sinaloa)]]></kwd>
</kwd-group>
</article-meta>
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