Skew-Elliptical Distributions and Their Applications: A Journey Beyond Normality

By Paul Embrechts. 2005

163 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/f01cebfbe5b9315f4409184af152dd0753ad8baa

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Citations in the corpus

A generalized class of skew distributions and associated robust quantile regression models (2014, background): “This may (or may not) result in the skew-elliptical distributions as in Genton (2004) and Liu & Dey (2004).”
Automated high-dimensional flow cytometric data analysis (2009, methodology): “To enable unsupervised learning of non-Gaussian populations, our multivariate skew t modeling methods use theoretically wellstudied high-dimensional distributions (13) that are robust against outliers and skew.”
On spatial skew‐Gaussian processes and applications (2009, background): “Keywords: EM algorithm, Matérn covariogram, Skew-normal distribution, Skew- Gaussian process, Slice sampling”
A skew item response model (2006, methodology): “Another algorithm to compute the cdf of the skew-normal distribution is based on the use of Owens function (see Azzalini 1985; Dalla-Valle 2004) and is available for R and Matlab programs.”
Robust linear mixed models with skew-normal independent distributions from a Bayesian perspective (2009, unknown): “Ma et al. (2004) consider a generalized flexible skew-elliptical distribution for the random effects density and proposed algorithms for maximum likelihood (ML) estimation and Bayesian inference via Markov Chain Monte Carlo (MCMC).”
Modeling jointly low, moderate, and heavy rainfall intensities without a threshold selection (2016, background): “The main idea of equation (2), basically multiplying a density like hn(.) by another function, has been extensively studied in the socalled skewed distributions research field [e.g., Genton, 2004].”