A Minimum variance approach to multivariate linear regression with application to actuarial problems

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<dc:creator>Landsman, Zinoviy</dc:creator>
<dc:creator>Makov, Udi E.</dc:creator>
<dc:date>2026-12-15</dc:date>
<dc:description xml:lang="es">Sumario: Variability is inherent in statistical, actuarial, and economic models, necessitating precise quantification for informed decision-making and risk management. Recently, Landsman and Shushi introduced the Location of Minimum Variance Squared Distance (LVS) risk functional, a novel variance-based measure of variability. We extend LVS to assess variability in regression models commonly used in actuarial analysis, enabling the construction of regression-type predictors in the Minimum Variance Squared Deviation (MVS) sense. We show that when the predicted vector Y follows a symmetric distribution, MVS aligns with the traditional Minimum Expected Squared Deviation (MES) functional. However, for non-symmetric distributions, MVS and MES diverge, with differences influenced by the joint third-moment matrix of distribution P and the covariance matrix of Y. We derive an analytical expression for MVS and explore a hybrid approach combining MVS and MES functionals. To illustrate the applicability of our approach, we present two numerical examples: (i) predicting three components of fire lossesbuildings, contents, and profitsand (ii) forecasting returns for six market indices based on the returns of their dominant stocks</dc:description>
<dc:identifier>https://documentacion.fundacionmapfre.org/documentacion/publico/es/bib/189866.do</dc:identifier>
<dc:language>eng</dc:language>
<dc:rights xml:lang="es">InC - http://rightsstatements.org/vocab/InC/1.0/</dc:rights>
<dc:subject xml:lang="es">Cálculo actuarial</dc:subject>
<dc:subject xml:lang="es">Análisis multivariante</dc:subject>
<dc:subject xml:lang="es">Predicciones estadísticas</dc:subject>
<dc:subject xml:lang="es">Gerencia de riesgos</dc:subject>
<dc:subject xml:lang="es">Análisis de varianza</dc:subject>
<dc:subject xml:lang="es">Big data</dc:subject>
<dc:type xml:lang="es">Artículos y capítulos</dc:type>
<dc:title xml:lang="es">A Minimum variance approach to multivariate linear regression with application to actuarial problems</dc:title>
<dc:relation xml:lang="es">En: European Actuarial Journal. - Cham, Switzerland  : Springer Nature Switzerland AG,  2021-2022. - 15/12/2025 Volume 15 Issue 3 - December 2025 , 22 p.</dc:relation>
</rdf:Description>
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