An approach to modeling asymmetric multivariate spatial covariance structures

Bo Li; Hao Zhang

doi:10.1016/j.jmva.2011.05.010

An approach to modeling asymmetric multivariate spatial covariance structures

Bo Li, Hao Zhang

Research output: Contribution to journal › Article › peer-review

Abstract

We propose a framework in light of the delay effect to model the asymmetry of multivariate covariance functions that is often exhibited in real data. This general approach can endow any valid symmetric multivariate covariance function with the ability of modeling asymmetry and is very easy to implement. Our simulations and real data examples show that asymmetric multivariate covariance functions based on our approach can achieve remarkable improvements in prediction over symmetric models.

Original language	English (US)
Pages (from-to)	1445-1453
Number of pages	9
Journal	Journal of Multivariate Analysis
Volume	102
Issue number	10
DOIs	https://doi.org/10.1016/j.jmva.2011.05.010
State	Published - Nov 2011
Externally published	Yes

Keywords

Asymmetry
Bivariate matérn
Intrinsic model
Multivariate covariance function
Symmetry

ASJC Scopus subject areas

Statistics and Probability
Numerical Analysis
Statistics, Probability and Uncertainty

Online availability

10.1016/j.jmva.2011.05.010

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title = "An approach to modeling asymmetric multivariate spatial covariance structures",

abstract = "We propose a framework in light of the delay effect to model the asymmetry of multivariate covariance functions that is often exhibited in real data. This general approach can endow any valid symmetric multivariate covariance function with the ability of modeling asymmetry and is very easy to implement. Our simulations and real data examples show that asymmetric multivariate covariance functions based on our approach can achieve remarkable improvements in prediction over symmetric models.",

keywords = "Asymmetry, Bivariate mat{\'e}rn, Intrinsic model, Multivariate covariance function, Symmetry",

author = "Bo Li and Hao Zhang",

note = "Funding Information: Li{\textquoteright}s research was partially supported by NSF grant DMS-1007686 . Zhang{\textquoteright}s research was partially supported by NSF grant DMS-0833323 . The authors would like to thank the editors and the anonymous referees for their helpful comments and suggestions.",

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doi = "10.1016/j.jmva.2011.05.010",

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AU - Zhang, Hao

N1 - Funding Information: Li’s research was partially supported by NSF grant DMS-1007686 . Zhang’s research was partially supported by NSF grant DMS-0833323 . The authors would like to thank the editors and the anonymous referees for their helpful comments and suggestions.

PY - 2011/11

Y1 - 2011/11

N2 - We propose a framework in light of the delay effect to model the asymmetry of multivariate covariance functions that is often exhibited in real data. This general approach can endow any valid symmetric multivariate covariance function with the ability of modeling asymmetry and is very easy to implement. Our simulations and real data examples show that asymmetric multivariate covariance functions based on our approach can achieve remarkable improvements in prediction over symmetric models.

AB - We propose a framework in light of the delay effect to model the asymmetry of multivariate covariance functions that is often exhibited in real data. This general approach can endow any valid symmetric multivariate covariance function with the ability of modeling asymmetry and is very easy to implement. Our simulations and real data examples show that asymmetric multivariate covariance functions based on our approach can achieve remarkable improvements in prediction over symmetric models.

KW - Asymmetry

KW - Bivariate matérn

KW - Intrinsic model

KW - Multivariate covariance function

KW - Symmetry

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JF - Journal of Multivariate Analysis

IS - 10

ER -

An approach to modeling asymmetric multivariate spatial covariance structures

Abstract

Keywords

ASJC Scopus subject areas

Online availability

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