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Open AccessJournal ArticleDOI

A General Class of Multivariate Skew-Elliptical Distributions

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TLDR
In this article, a general class of multivariate skew-elliptical distributions is proposed, which contains the multivariate normal, Student's t, exponential power, and Pearson type II, with an extra parameter to regulate skewness.
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This article is published in Journal of Multivariate Analysis.The article was published on 2001-10-01 and is currently open access. It has received 629 citations till now. The article focuses on the topics: Elliptical distribution & Multivariate stable distribution.

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Citations
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Journal ArticleDOI

Finite mixture modeling using shape mixtures of the skew scale mixtures of normal distributions

TL;DR: A finite mixture model is presented for this new family of distributions, which is a novel model-based tool for clustering heterogeneous data in the presence of skewed and heavy-tailed outcomes.
Journal ArticleDOI

Linear censored regression models with skew scale mixtures of normal distributions

TL;DR: A special source of difficulty in the statistical analysis is the possibility that some subjects may not have a complete observation of the response variable as discussed by the authors, which makes it difficult for statistical analysis.

Robust estimation of mixtures of Skew Normal Distributions

TL;DR: Robust estimation of mixtures of skew normal is introduced, to resist sparse outliers and even pointwise contamination that could arise in data collection.
Journal Article

Generalizations of the Skew t-Normal Distribution and their Properties

TL;DR: In this paper, several generalizations of the skew t-normal distribution, and some of their properties are considered, and several theorems for constructing each generalized skew tnormal distribution are presented.
Posted Content

A Non-Gaussian Spatio-Temporal Model for Daily Wind Speeds Based on a Multivariate Skew-t Distribution

TL;DR: In this article, the authors examined the impact of internal climate variability on seasonal wind power density fluctuations over Saudi Arabia using 30 simulations from the Large Ensemble Project (LENS) developed at the National Center for Atmospheric Research.
References
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MonographDOI

Stable Non-Gaussian Random Processes : Stochastic Models with Infinite Variance

TL;DR: In this paper, the authors introduce sample path properties such as boundedness, continuity, and oscillations, as well as integrability, and absolute continuity of the path in the real line.
Book

Symmetric Multivariate and Related Distributions

TL;DR: In this article, the authors define marginal distributions, moments and density marginal distributions moments density the relationship between (phi and f) conditional distributions properties of elliptically symmetric distributions mixtures of normal distributions robust statistics and regression model robust statistics regression model log-elliptical and additive logistic elliptical distributions multivariate log elliptical distribution additive logistics elliptical distribution complex elliptical symmetric distribution.
Journal ArticleDOI

The multivariate skew-normal distribution

Adelchi Azzalini, +1 more
- 01 Dec 1996 - 
TL;DR: In this article, a multivariate parametric family such that the marginal densities are scalar skew-normal is introduced, and its properties are studied with special emphasis on the bivariate case.
Journal ArticleDOI

Statistical applications of the multivariate skew normal distribution

TL;DR: Azzalini and Dalla Valle as discussed by the authors have discussed the multivariate skew normal distribution which extends the class of normal distributions by the addition of a shape parameter, and a further extension is described which introduces a skewing factor of an elliptical density.
Journal ArticleDOI

Statistical applications of the multivariate skew-normal distribution

TL;DR: Azzalini and Dalla Valle as mentioned in this paper have recently discussed the multivariate skew-normal distribution which extends the class of normal distributions by the addition of a shape parameter.
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