Results 201 to 210 of about 115,259 (238)
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MULTIVARIATE SKEW t -DISTRIBUTION
Statistics: A Journal of Theoretical and Applied Statistics, 2003In this paper, we define multivariate skew t-distribution which has some of the properties of multivariate t-distribution and has a shape parameter to represent skewness. Some of its properties are also studied including the moments. Multivariate skew-Cauchy distribution is given as a special case.
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On the distributions of multivariate sample skewness
Journal of Statistical Planning and Inference, 2010zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Okamoto, Naoya, Seo, Takashi
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The multivariate skew-slash distribution
Journal of Statistical Planning and Inference, 2006zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Wang, Jing, Genton, Marc G.
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Some Skewed Multivariate Distributions
American Journal of Mathematical and Management Sciences, 2000SYNOPTIC ABSTRACTHidden marginal truncation models are investigated in non-normal settings. The univariate skew normal distribution as described by Azzalini (1985) is generalized to non-normal variants, which coincide with t he form of the skew normal if the underlying distributions are symmetric.
Barry C. Arnold, Robert J. Beaver
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The multivariate skew-slash t and skew-slash Cauchy distributions
Model Assisted Statistics and Applications, 2012The slash distributions are flexible distributions that can take skewness and heavy tails into account. In this article we define skewed versions of multivariate slash t distribution and multivariate slash Cauchy distribution. These distributions belong to the multivariate skew-slash elliptical family.
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Multivariate skew-normal distribution for modelling skewed spatial data
Spatial and Spatio-temporal EpidemiologyMultivariate spatial data are commonly modelled using the shared spatial component and multivariate intrinsic conditional autoregressive (MICAR) models where the spatial random variables are assumed to be normally distributed. However, the normality assumption may not be always right as the spatially structured component may show non-normal ...
Kassahun Abere Ayalew +2 more
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On a measure of multivariate skewness and a test for multivariate normality
Annals of the Institute of Statistical Mathematics, 1982We consider an extension of Pearson measure of skewness to a multivariate case and apply the proposed measure to a test of multivariate normality.
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Accelerating the Multivariate SKEW T Parameter Estimation
2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), 2019This paper considers an acceleration scheme for the multivariate skew $t$ (MST) parameter estimation. MST is a heavy-tailed distribution allowing also for skewness. The distribution is very convenient for many real-life applications where there data is heavy-tailed, there may be outliers, and data may be skewed. One example is financial data.
Rui Zhou, Daniel P. Palomar
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Regularized skewness parameter estimation for multivariate skew normal and skew t distributions
2019<p>The skewed normal (SN) distribution introduced by Azzalini has opened a new era for analyzing skewed data. The idea behind it is that it incorporates a new parameter regulating shape and skewness on the symmetric Gaussian distribution. This idea was soon extended to other symmetric distributions such as the Student's t distribution, resulting ...
Sheng Wang +5 more
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The Skew-normal Distribution and Related Multivariate Families*
Scandinavian Journal of Statistics, 2005If \(f_0\) is a \(d\)-dimensional density with \(f_0(x)=f_0(-x)\), \(G\) is a one-dimensional CDF with symmetric about 0 PDF and \(w: R^d\to R\) is any function with \(w(-x)=-w(x)\), then \(f(z)=2f_0(z)G(w(z))\) is a density function on \(R^d\). With \(f_0\sim N(0,\Omega)\), \(G\sim N(0,1)\), and a linear function \(w\) one obtains \(f\) being a skew ...
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