Moments of skew-normal random vectors and their quadratic forms

Marc G. Genton; Li He; Xiangwei Liu

doi:10.1016/S0167-7152(00)00164-4

Moments of skew-normal random vectors and their quadratic forms

Marc G. Genton^*, Li He, Xiangwei Liu

^*Corresponding author for this work

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

113 Scopus citations

Abstract

In this paper, we derive the moments of random vectors with multivariate skew-normal distribution and their quadratic forms. Applications to time series and spatial statistics are discussed. In particular, it is shown that the moments of the sample autocovariance function and of the sample variogram estimator do not depend on the skewness vector.

Original language	English (US)
Pages (from-to)	319-325
Number of pages	7
Journal	Statistics and Probability Letters
Volume	51
Issue number	4
DOIs	https://doi.org/10.1016/S0167-7152(00)00164-4
State	Published - Feb 15 2001
Externally published	Yes

Keywords

Autocovariance function
Multivariate skew-normal distribution
Quadratic form
Variogram

ASJC Scopus subject areas

Statistics and Probability
Statistics, Probability and Uncertainty

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10.1016/S0167-7152(00)00164-4

Cite this

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abstract = "In this paper, we derive the moments of random vectors with multivariate skew-normal distribution and their quadratic forms. Applications to time series and spatial statistics are discussed. In particular, it is shown that the moments of the sample autocovariance function and of the sample variogram estimator do not depend on the skewness vector.",

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author = "Genton, {Marc G.} and Li He and Xiangwei Liu",

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AU - Genton, Marc G.

AU - He, Li

AU - Liu, Xiangwei

PY - 2001/2/15

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N2 - In this paper, we derive the moments of random vectors with multivariate skew-normal distribution and their quadratic forms. Applications to time series and spatial statistics are discussed. In particular, it is shown that the moments of the sample autocovariance function and of the sample variogram estimator do not depend on the skewness vector.

AB - In this paper, we derive the moments of random vectors with multivariate skew-normal distribution and their quadratic forms. Applications to time series and spatial statistics are discussed. In particular, it is shown that the moments of the sample autocovariance function and of the sample variogram estimator do not depend on the skewness vector.

KW - Autocovariance function

KW - Multivariate skew-normal distribution

KW - Quadratic form

KW - Variogram

UR - http://www.scopus.com/inward/record.url?scp=0001168061&partnerID=8YFLogxK

U2 - 10.1016/S0167-7152(00)00164-4

DO - 10.1016/S0167-7152(00)00164-4

M3 - Article

AN - SCOPUS:0001168061

SN - 0167-7152

VL - 51

SP - 319

EP - 325

JO - Statistics and Probability Letters

JF - Statistics and Probability Letters

IS - 4

ER -

Moments of skew-normal random vectors and their quadratic forms

Abstract

Keywords

ASJC Scopus subject areas

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