A Minimum variance approach to multivariate linear regression with application to actuarial problems
Contenido multimedia no disponible por derechos de autor o por acceso restringido. Contacte con la institución para más información.
| Tag | 1 | 2 | Valor |
|---|---|---|---|
| LDR | 00000cab a2200000 4500 | ||
| 001 | MAP20260006307 | ||
| 003 | MAP | ||
| 005 | 20260310165736.0 | ||
| 008 | 260225e20261215che|||p |0|||b|eng d | ||
| 040 | $aMAP$bspa$dMAP | ||
| 084 | $a6 | ||
| 100 | $0MAPA20110013387$aLandsman, Zinoviy | ||
| 245 | 1 | 2 | $aA Minimum variance approach to multivariate linear regression with application to actuarial problems$cZinoviy Landsman and Udi Makov |
| 520 | $aVariability 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 | ||
| 650 | 4 | $0MAPA20080579258$aCálculo actuarial | |
| 650 | 4 | $0MAPA20080604721$aAnálisis multivariante | |
| 650 | 4 | $0MAPA20120011137$aPredicciones estadísticas | |
| 650 | 4 | $0MAPA20080591182$aGerencia de riesgos | |
| 650 | 4 | $0MAPA20080594633$aAnálisis de varianza | |
| 650 | 4 | $0MAPA20140022717$aBig data | |
| 700 | 1 | $0MAPA20110013424$aMakov, Udi E. | |
| 773 | 0 | $wMAP20220007085$g15/12/2025 Volume 15 Issue 3 - December 2025 , 22 p.$tEuropean Actuarial Journal$dCham, Switzerland : Springer Nature Switzerland AG, 2021-2022 |