A Stable approach based on asymptotic space integration for moment independent uncertainty importance measure
<?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>Stable approach based on asymptotic space integration for moment independent uncertainty importance measure</title>
</titleInfo>
<name type="personal" usage="primary" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20140008131">
<namePart>Xu, Xin</namePart>
<nameIdentifier>MAPA20140008131</nameIdentifier>
</name>
<typeOfResource>text</typeOfResource>
<genre authority="marcgt">periodical</genre>
<originInfo>
<place>
<placeTerm type="code" authority="marccountry">esp</placeTerm>
</place>
<dateIssued encoding="marc">2014</dateIssued>
<issuance>serial</issuance>
</originInfo>
<language>
<languageTerm type="code" authority="iso639-2b">spa</languageTerm>
</language>
<physicalDescription>
<form authority="marcform">print</form>
</physicalDescription>
<abstract displayLabel="Summary">The error estimate of Borgonovo's moment-independent index inline image is considered, and it shows that the possible computational complexity of inline image is mainly due to the probability density function (PDF) estimate because the PDF estimate is an ill-posed problem and its convergence rate is quite slow. So it reminds us to compute Borgonovo's index using other methods. To avoid the PDF estimate, inline image, which is based on the PDF, is first approximatively represented by the cumulative distribution function (CDF). The CDF estimate is well posed and its convergence rate is always faster than that of the PDF estimate. From the representation, a stable approach is proposed to compute inline image with an adaptive procedure. Since the small probability multidimensional integral needs to be computed in this procedure, a computational strategy named asymptotic space integration is introduced to reduce a high-dimensional integral to a one-dimensional integral. Then we can compute the small probability multidimensional integral by adaptive numerical integration in one dimension with an improved convergence rate. From the comparison of numerical error analysis of some examples, it can be shown that the proposed method is an effective approach to uncertainty importance measure computation.</abstract>
<note type="statement of responsibility">Xin Xu, Zhenzhou Lu, Xiaopeng Luo</note>
<classification authority="">7</classification>
<location>
<url displayLabel="MÁS INFORMACIÓN" usage="primary display">mailto:centrodocumentacion@fundacionmapfre.org?subject=Consulta%20de%20una%20publicaci%C3%B3n%20&body=Necesito%20m%C3%A1s%20informaci%C3%B3n%20sobre%20este%20documento%3A%20%0A%0A%5Banote%20aqu%C3%AD%20el%20titulo%20completo%20del%20documento%20del%20que%20desea%20informaci%C3%B3n%20y%20nos%20pondremos%20en%20contacto%20con%20usted%5D%20%0A%0AGracias%20%0A</url>
</location>
<relatedItem type="host">
<titleInfo>
<title>Risk analysis : an international journal</title>
</titleInfo>
<originInfo>
<publisher>McLean, Virginia : Society for Risk Analysis, 1987-2015</publisher>
</originInfo>
<identifier type="issn">0272-4332</identifier>
<identifier type="local">MAP20077000345</identifier>
<part>
<text>03/02/2014 Volumen 34 Número 2 - febrero 2014 </text>
</part>
</relatedItem>
<recordInfo>
<recordContentSource authority="marcorg">MAP</recordContentSource>
<recordCreationDate encoding="marc">140509</recordCreationDate>
<recordChangeDate encoding="iso8601">20140520144337.0</recordChangeDate>
<recordIdentifier source="MAP">MAP20140015832</recordIdentifier>
<languageOfCataloging>
<languageTerm type="code" authority="iso639-2b">spa</languageTerm>
</languageOfCataloging>
</recordInfo>
</mods>
</modsCollection>