Family of extended mean mixtures of multivariate normal distributions: Properties, inference and applications

Guangshuai Zhou; Chuancun Yin; Guangshuai Zhou; Chuancun Yin

doi:10.3934/math.2022688

AIMS Mathematics

2022, Volume 7, Issue 7: 12390-12414. doi: 10.3934/math.2022688

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Research article

Family of extended mean mixtures of multivariate normal distributions: Properties, inference and applications

Guangshuai Zhou ,
Chuancun Yin ^,

School of Statistics and Data Science, Qufu Normal University, Qufu 273165, China

Received: 07 March 2022 Revised: 20 April 2022 Accepted: 22 April 2022 Published: 26 April 2022
MSC : 60E07, 60E10, 62H05, 62H10, 62H12

A new class of skewed distributions, with a matrix skewness parameter, called extended mean mixtures of multivariate normal (EMMN) distributions, is constructed. The family of EMMN distributions includes the SN and MMN distributions as special cases. Some basic properties of this family, such as characteristic function, moment generating function, affine transformation and canonical forms of the distributions are derived. An EM-type algorithm is developed to carry out the maximum likelihood estimation of the parameters. Two special cases of this family are studied in detail. A simulation is carried out to examine the performance of the estimation method, and the flexibility is illustrated by fitting a special case of this family to a real data. Finally, the theoretical formula of the multivariate tail conditional expectation of the EMMN distribution is derived.
- EM algorithm,
- matrix skewness parameter,
- mean mixtures of multivariate normal distribution,
- multivariate tail conditional expectation,
- risk measure
Citation: Guangshuai Zhou, Chuancun Yin. Family of extended mean mixtures of multivariate normal distributions: Properties, inference and applications[J]. AIMS Mathematics, 2022, 7(7): 12390-12414. doi: 10.3934/math.2022688

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Abstract

A new class of skewed distributions, with a matrix skewness parameter, called extended mean mixtures of multivariate normal (EMMN) distributions, is constructed. The family of EMMN distributions includes the SN and MMN distributions as special cases. Some basic properties of this family, such as characteristic function, moment generating function, affine transformation and canonical forms of the distributions are derived. An EM-type algorithm is developed to carry out the maximum likelihood estimation of the parameters. Two special cases of this family are studied in detail. A simulation is carried out to examine the performance of the estimation method, and the flexibility is illustrated by fitting a special case of this family to a real data. Finally, the theoretical formula of the multivariate tail conditional expectation of the EMMN distribution is derived.

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