A note on joint mix random vectors

Yugu Xiao; Jing Yao

doi:10.1080/03610926.2019.1586937

A note on joint mix random vectors

Yugu Xiao, Jing Yao^*

^*Corresponding author for this work

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

1 Citation (Scopus)

Abstract

This note studies the dependence of joint mix random vectors from the perspective of covariance matrix. We first provide two useful methods in simulations to construct joint mix for Normal distribution. Then, we propose to characterize joint mix by covariance matrix for general marginal distribution. We present some examples showing that our methodology could provide supplementary results to relevant studies in literature.

Original language	English
Pages (from-to)	3063-3072
Number of pages	10
Journal	Communications in Statistics - Theory and Methods
Volume	49
Issue number	12
Early online date	3 Apr 2019
DOIs	https://doi.org/10.1080/03610926.2019.1586937
Publication status	Published - 17 Jun 2020

Keywords

copula
covariance matrix
Dependence
joint mixability

ASJC Scopus subject areas

Statistics and Probability

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10.1080/03610926.2019.1586937

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title = "A note on joint mix random vectors",

abstract = "This note studies the dependence of joint mix random vectors from the perspective of covariance matrix. We first provide two useful methods in simulations to construct joint mix for Normal distribution. Then, we propose to characterize joint mix by covariance matrix for general marginal distribution. We present some examples showing that our methodology could provide supplementary results to relevant studies in literature.",

keywords = "copula, covariance matrix, Dependence, joint mixability",

author = "Yugu Xiao and Jing Yao",

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AU - Yao, Jing

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AB - This note studies the dependence of joint mix random vectors from the perspective of covariance matrix. We first provide two useful methods in simulations to construct joint mix for Normal distribution. Then, we propose to characterize joint mix by covariance matrix for general marginal distribution. We present some examples showing that our methodology could provide supplementary results to relevant studies in literature.

KW - copula

KW - covariance matrix

KW - Dependence

KW - joint mixability

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U2 - 10.1080/03610926.2019.1586937

DO - 10.1080/03610926.2019.1586937

M3 - Article

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SP - 3063

EP - 3072

JO - Communications in Statistics - Theory and Methods

JF - Communications in Statistics - Theory and Methods

IS - 12

ER -

A note on joint mix random vectors

Abstract

Keywords

ASJC Scopus subject areas

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