284 citations · 806 across the 87 of their papers we have counts for
6 papers · 2 filters
Visualization and Assessment of Copula Symmetry
Cristian F. Jimenez-Varon, Hao Lee, Marc G. Genton +1
Visualization and assessment of copula structures are crucial for accurately understanding and modeling the dependencies in multivariate data analysis. In this paper, we introduce…
Multivariate Unified Skew-t Distributions And Their Properties
Kesen Wang, Maicon J. Karling, Reinaldo B. Arellano-Valle +1
The unified skew-t (SUT) is a flexible parametric multivariate distribution that accounts for skewness and heavy tails in the data. A few of its properties can be found scattered i…
A Multivariate Skew-Normal-Tukey-h Distribution
Sagnik Mondal, Marc G. Genton
We introduce a new family of multivariate distributions by taking the component-wise Tukey-h transformation of a random vector following a skew-normal distribution. The proposed di…
A Neural Network-Based Approach to Normality Testing for Dependent Data
Minwoo Kim, Marc G Genton, Raphael Huser +1
There is a wide availability of methods for testing normality under the assumption of independent and identically distributed data. When data are dependent in space and/or time, ho…
Which Parameterization of the Matérn Covariance Function?
Kesen Wang, Sameh Abdulah, Ying Sun +1
The Matérn family of covariance functions is currently the most popularly used model in spatial statistics, geostatistics, and machine learning to specify the correlation between t…
Goodness-of-fit tests for multivariate skewed distributions based on the characteristic function
Maicon J. Karling, Marc G. Genton, Simos G. Meintanis
We employ a general Monte Carlo method to test composite hypotheses of goodness-of-fit for several popular multivariate models that can accommodate both asymmetry and heavy tails.…