<?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>2518-4431</journal-id>
<journal-title><![CDATA[Investigación & Desarrollo]]></journal-title>
<abbrev-journal-title><![CDATA[Inv. y Des.]]></abbrev-journal-title>
<issn>2518-4431</issn>
<publisher>
<publisher-name><![CDATA[UNIVERSIDAD PRIVADA BOLIVIANA]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2518-44312025000200004</article-id>
<article-id pub-id-type="doi">10.23881/idupbo.025.2-3e</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Datos faltantes y análisis multidimensional con el método Alkire-Foster: Una revisión de la literatura]]></article-title>
<article-title xml:lang="en"><![CDATA[Missing data and multidimensional analysis with the Alkire-Foster method: A literature review]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Revilla Cabrera]]></surname>
<given-names><![CDATA[Daniel Alejandro]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Privada Boliviana (UPB)  ]]></institution>
<addr-line><![CDATA[ ]]></addr-line>
<country>Bolivia</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>00</month>
<year>2025</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>00</month>
<year>2025</year>
</pub-date>
<volume>25</volume>
<numero>2</numero>
<fpage>4</fpage>
<lpage>15</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.bo/scielo.php?script=sci_arttext&amp;pid=S2518-44312025000200004&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.bo/scielo.php?script=sci_abstract&amp;pid=S2518-44312025000200004&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.bo/scielo.php?script=sci_pdf&amp;pid=S2518-44312025000200004&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen: Este artículo aborda el problema de los datos faltantes en la medición de pobreza multidimensional, con especial atención a su impacto sobre la validez axiomática del método Alkire-Foster, empleado regularmente para medir este concepto amplio de pobreza. Se sostiene que la ausencia de datos no es un problema puramente técnico y proclive a generar sesgos, sino que también puede comprometer la identificación de privaciones y vulnerar alguno de los axiomas fundamentales del método: monotonicidad, descomposición por subgrupos y descomposición dimensional. A partir de una revisión crítica de la literatura, se comparan cuatro enfoques de imputación: análisis de casos completos, imputación múltiple por ecuaciones encadenadas (MICE), técnicas de descomposición matricial (como Soft-Impute) y redes neuronales bayesianas. Se discuten sus fortalezas y limitaciones en relación con la preservación de las propiedades axiomáticas mencionadas. Además, se presentan argumentos a favor de una metodología híbrida que combine algoritmos imputativos con criterios de validación normativa para garantizar coherencia ética y solidez inferencial. El artículo concluye que la elección de métodos de imputación en contextos sociales debe considerar no solo la eficiencia estadística, sino también la compatibilidad estructural con el modelo teórico que subyace el ejercicio de medición de la pobreza. Se proponen líneas futuras para el desarrollo de marcos integradores que articulen métodos de imputación, el respeto de axiomas y el uso en política pública.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract: This article addresses the problem of missing data in measuring multidimensional poverty, with particular attention to its impact on the axiomatic validity of the Alkire-Foster method, regularly used to measure this broad concept of poverty. It argues that the absence of data is not purely a technical problem prone to generating bias, but can also compromise the identification of deprivation and violate some of the fundamental axioms of the method: monotonicity, decomposition by subgroups, and dimensional decomposition. Based on a critical review of the literature, four imputation approaches are compared: complete case analysis, multiple imputation by chained equations (MICE), matrix decomposition techniques (such as Soft-Impute), and Bayesian neural networks. Their strengths and limitations in relation to the preservation of the aforementioned axiomatic proper- ties are discussed. In addition, arguments are presented in favor of a hybrid methodology that combines imputation algorithms with normative validation criteria to ensure ethical consistency and inferential robustness. The article concludes that the choice of imputation methods in social contexts should consider not only statistical efficiency but also structural compatibility with the theoretical model underlying the poverty measurement exercise. Future lines of research are proposed for the development of integrative frameworks that articulate imputation methods, respect for axioms, and use in public policy.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Datos faltantes]]></kwd>
<kwd lng="es"><![CDATA[pobreza multidimensional]]></kwd>
<kwd lng="es"><![CDATA[Alkire-Foster]]></kwd>
<kwd lng="es"><![CDATA[imputación múltiple]]></kwd>
<kwd lng="es"><![CDATA[ética del dato]]></kwd>
<kwd lng="es"><![CDATA[redes neuronales bayesianas]]></kwd>
<kwd lng="en"><![CDATA[Missing data]]></kwd>
<kwd lng="en"><![CDATA[multidimensional poverty]]></kwd>
<kwd lng="en"><![CDATA[Alkire-Foster]]></kwd>
<kwd lng="en"><![CDATA[multiple imputation]]></kwd>
<kwd lng="en"><![CDATA[data ethics]]></kwd>
<kwd lng="en"><![CDATA[Bayesian neural networks]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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