Optimal singular correlation matrices estimated when sample size is less than the number of random variables

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26110%2F12%3APU101551" target="_blank" >RIV/00216305:26110/12:PU101551 - isvavai.cz</a>

Result on the web

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DOI - Digital Object Identifier

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Result language

angličtina

Original language name

Optimal singular correlation matrices estimated when sample size is less than the number of random variables

Original language description

This paper presents a number of theoretical and numerical results for two norms of optimal correlation matrices in relation to correlation control in Monte Carlo type sampling and the designs of experiments. The optimal correlation matrices are constructed for cases when the number of simulations (experiments) Nsim is less than or equal to the stochastic dimension, i.e. the number of random variables (factors) Nvar. In such cases the estimated correlation matrix cannot be positive definite and must be singular. However, the correlation matrix may be required to be as close to the unit matrix as possible (optimal). The paper presents a simple mechanical analogy for such optimal singular positive semidefinite correlation matrices. Many examples of optimal correlation matrices are given, both analytically and numerically.

Czech name

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Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

BB - Applied statistics, operational research

OECD FORD branch

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Project

<a href="/en/project/GAP105%2F11%2F1385" target="_blank" >GAP105/11/1385: Inverse structural reliability problems</a><br>

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2012

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

PROBABILISTIC ENGINEERING MECHANICS

ISSN

0266-8920

e-ISSN

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Volume of the periodical

2012 (30)

Issue of the periodical within the volume

1

Country of publishing house

GB - UNITED KINGDOM

Number of pages

13

Pages from-to

104-116

UT code for WoS article

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EID of the result in the Scopus database

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