A tuning parameter free test for properties of space-time covariance functions

Xiaofeng Shao; Bo Li

doi:10.1016/j.jspi.2009.05.008

A tuning parameter free test for properties of space-time covariance functions

Xiaofeng Shao, Bo Li

Statistics

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

Abstract

We propose a new nonparametric test to test for symmetry and separability of space-time covariance functions. Unlike the existing nonparametric tests, our test has the attractive convenience of being free of choosing any user-chosen number or smoothing parameter. The asymptotic null distributions of the test statistics are free of nuisance parameters and the critical values have been tabulated in the literature. From a practical point of view, our test is easy to implement and can be readily used by the practitioner. A Monte-Carlo experiment and real data analysis illustrate the finite sample performance of the new test.

Original language	English (US)
Pages (from-to)	4031-4038
Number of pages	8
Journal	Journal of Statistical Planning and Inference
Volume	139
Issue number	12
DOIs	https://doi.org/10.1016/j.jspi.2009.05.008
State	Published - Dec 1 2009

Keywords

Asymptotically pivotal
Covariance
Full symmetry
Separability

ASJC Scopus subject areas

Statistics and Probability
Statistics, Probability and Uncertainty
Applied Mathematics

Online availability

10.1016/j.jspi.2009.05.008

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abstract = "We propose a new nonparametric test to test for symmetry and separability of space-time covariance functions. Unlike the existing nonparametric tests, our test has the attractive convenience of being free of choosing any user-chosen number or smoothing parameter. The asymptotic null distributions of the test statistics are free of nuisance parameters and the critical values have been tabulated in the literature. From a practical point of view, our test is easy to implement and can be readily used by the practitioner. A Monte-Carlo experiment and real data analysis illustrate the finite sample performance of the new test.",

keywords = "Asymptotically pivotal, Covariance, Full symmetry, Separability",

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note = "Funding Information: The authors are grateful to an anonymous referee for helpful comments. Shao acknowledges partial support from National Science Foundation Grants DMS-0804937 and CMG-0724752. ",

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AU - Shao, Xiaofeng

AU - Li, Bo

N1 - Funding Information: The authors are grateful to an anonymous referee for helpful comments. Shao acknowledges partial support from National Science Foundation Grants DMS-0804937 and CMG-0724752.

PY - 2009/12/1

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N2 - We propose a new nonparametric test to test for symmetry and separability of space-time covariance functions. Unlike the existing nonparametric tests, our test has the attractive convenience of being free of choosing any user-chosen number or smoothing parameter. The asymptotic null distributions of the test statistics are free of nuisance parameters and the critical values have been tabulated in the literature. From a practical point of view, our test is easy to implement and can be readily used by the practitioner. A Monte-Carlo experiment and real data analysis illustrate the finite sample performance of the new test.

AB - We propose a new nonparametric test to test for symmetry and separability of space-time covariance functions. Unlike the existing nonparametric tests, our test has the attractive convenience of being free of choosing any user-chosen number or smoothing parameter. The asymptotic null distributions of the test statistics are free of nuisance parameters and the critical values have been tabulated in the literature. From a practical point of view, our test is easy to implement and can be readily used by the practitioner. A Monte-Carlo experiment and real data analysis illustrate the finite sample performance of the new test.

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KW - Full symmetry

KW - Separability

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A tuning parameter free test for properties of space-time covariance functions

Abstract

Keywords

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