284 citations · 806 across the 87 of their papers we have counts for
10 papers · 1 filter
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…
Portability and Scalability Evaluation of Large-Scale Statistical Modeling and Prediction Software through HPC-Ready Containers
Sameh Abdulah, Jorge Ejarque, Omar Marzouk +5
HPC-based applications often have complex workflows with many software dependencies that hinder their portability on contemporary HPC architectures. In addition, these applications…
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…
Efficient stochastic generators with spherical harmonic transformation for high-resolution global climate simulations from CESM2-LENS2
Yan Song, Zubair Khalid, Marc G. Genton
Earth system models (ESMs) are fundamental for understanding Earth's complex climate system. However, the computational demands and storage requirements of ESM simulations limit th…
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…
Efficient Large-scale Nonstationary Spatial Covariance Function Estimation Using Convolutional Neural Networks
Pratik Nag, Yiping Hong, Sameh Abdulah +3
Spatial processes observed in various fields, such as climate and environmental science, often occur on a large scale and demonstrate spatial nonstationarity. Fitting a Gaussian pr…
On the non-identifiability of unified skew-normal distributions
Kesen Wang, Reinaldo B. Arellano-Valle, Adelchi Azzalini +1
In this note, we investigate the non-identifiability of the multivariate unified skew-normal distribution under permutation of its latent variables. We show that the non-identifiab…
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.…