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

<?xml version="1.0" encoding="UTF-8"?><modsCollection xmlns="http://www.loc.gov/mods/v3" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-8.xsd">
<mods version="3.8">
<titleInfo>
<nonSort xml:space="preserve">A  </nonSort>
<title>Minimum variance approach to multivariate linear regression with application to actuarial problems</title>
</titleInfo>
<name type="personal" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20110013424">
<namePart>Makov, Udi E.</namePart>
<nameIdentifier>MAPA20110013424</nameIdentifier>
</name>
<name type="corporate" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20180008764">
<namePart>Springer</namePart>
<nameIdentifier>MAPA20180008764</nameIdentifier>
</name>
<typeOfResource>text</typeOfResource>
<genre authority="marcgt">periodical</genre>
<originInfo>
<place>
<placeTerm type="code" authority="marccountry">che</placeTerm>
</place>
<dateIssued encoding="marc">2025</dateIssued>
<issuance>serial</issuance>
</originInfo>
<language>
<languageTerm type="code" authority="iso639-2b">eng</languageTerm>
</language>
<physicalDescription>
<form authority="marcform">print</form>
</physicalDescription>
<abstract displayLabel="Summary">This article introduces a new multivariate linear regression approach based on minimizing the variance of the squared distance (MVS), as an alternative to the classical minimum expected squared deviation (MES) criterion. The methodology enhances the accuracy of predicting risk vectors when the underlying distributions are non-symmetric by incorporating third-order moment information. Closed-form analytical expressions for the estimators are derived, and the method is shown to significantly reduce prediction variability. The study includes two real-world applications-fire insurance losses and stock index returns-demonstrating the potential of this approach for actuarial and financial modeling</abstract>
<note type="statement of responsibility">Zinoviy Landsman and Udi Makov</note>
<subject xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20080592011">
<topic>Modelos actuariales</topic>
</subject>
<subject xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20080604721">
<topic>Análisis multivariante</topic>
</subject>
<subject xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20080582418">
<topic>Riesgo financiero</topic>
</subject>
<subject xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20080597665">
<topic>Métodos estadísticos</topic>
</subject>
<subject xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20080594633">
<topic>Análisis de varianza</topic>
</subject>
<subject xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20080557799">
<topic>Dependencia</topic>
</subject>
<classification authority="">6</classification>
<relatedItem type="host">
<titleInfo>
<title>European Actuarial Journal</title>
</titleInfo>
<originInfo>
<publisher>Cham, Switzerland  : Springer Nature Switzerland AG,  2021-2022</publisher>
</originInfo>
<identifier type="local">MAP20220007085</identifier>
<part>
<text>11/08/2025 Volume 15 - Number 2 - August  2025 , p. 899 - 920</text>
</part>
</relatedItem>
<recordInfo>
<recordContentSource authority="marcorg">MAP</recordContentSource>
<recordCreationDate encoding="marc">260206</recordCreationDate>
<recordChangeDate encoding="iso8601">20260211184455.0</recordChangeDate>
<recordIdentifier source="MAP">MAP20260002996</recordIdentifier>
<languageOfCataloging>
<languageTerm type="code" authority="iso639-2b">spa</languageTerm>
</languageOfCataloging>
</recordInfo>
</mods>
</modsCollection>