284 citations · 806 across the 87 of their papers we have counts for
43 papers · 1 filter
Rate-Preserving Shrinking-Support Gaussian Process Prediction
Xiaopeng Xiang, Wenlin Dai, Marc G. Genton +1
Gaussian process prediction is a central tool in spatial statistics, but standard implementations require dense matrix operations that become prohibitive for large datasets. We pro…
Uniform-over-dimension location tests for multivariate and high-dimensional data
Ritabrata Karmakar, Joydeep Chowdhury, Subhajit Dutta +1
Asymptotic methods for hypothesis testing in high-dimensional data usually require the dimension of the observations to increase to infinity, often with an additional relationship…
Scalable Asynchronous Federated Modeling for Spatial Data
Jianwei Shi, Sameh Abdulah, Ying Sun +1
Spatial data are central to applications such as environmental monitoring and urban planning, but are often distributed across devices where privacy and communication constraints l…
Robust Maximum -Likelihood Covariance Estimation for Replicated Spatial Data
Sihan Chen, Joydeep Chowdhury, Marc G. Genton
Parameter estimation with the maximum -likelihood estimator (MLE) is an alternative to the maximum likelihood estimator (MLE) that considers the -th power of the likelih…
Conjugacy properties of multivariate unified skew-elliptical distributions
Maicon J. Karling, Daniele Durante, Marc G. Genton
The broad class of multivariate unified skew-normal (SUN) distributions has been recently shown to possess important conjugacy properties. When used as priors for the coefficients…
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.…
Global Depths for Irregularly Observed Multivariate Functional Data
Zhuo Qu, Wenlin Dai, Marc G. Genton
Two frameworks for multivariate functional depth based on multivariate depths are introduced in this paper. The first framework is multivariate functional integrated depth, and the…
Nonseparable Space-Time Stationary Covariance Functions on Networks cross Time
Emilio Porcu, Philip A. White, Marc G. Genton
The advent of data science has provided an increasing number of challenges with high data complexity. This paper addresses the challenge of space-time data where the spatial domain…
Multivariate Functional Outlier Detection using the FastMUOD Indices
Oluwasegun Taiwo Ojo, Antonio Fernández Anta, Marc G. Genton +1
We present definitions and properties of the fast massive unsupervised outlier detection (FastMUOD) indices, used for outlier detection (OD) in functional data. FastMUOD detects ou…
Large-Scale Low-Rank Gaussian Process Prediction with Support Points
Yan Song, Wenlin Dai, Marc G. Genton
Low-rank approximation is a popular strategy to tackle the "big n problem" associated with large-scale Gaussian process regressions. Basis functions for developing low-rank structu…
Spatio-Temporal Cross-Covariance Functions under the Lagrangian Framework with Multiple Advections
Mary Lai O. Salvaña, Amanda Lenzi, Marc G. Genton
When analyzing the spatio-temporal dependence in most environmental and earth sciences variables such as pollutant concentrations at different levels of the atmosphere, a special p…
Robust Two-Layer Partition Clustering of Sparse Multivariate Functional Data
Zhuo Qu, Wenlin Dai, Marc G. Genton
A novel elastic time distance for sparse multivariate functional data is proposed and used to develop a robust distance-based two-layer partition clustering method. With this propo…
Scalable Gaussian-process regression and variable selection using Vecchia approximations
Jian Cao, Joseph Guinness, Marc G. Genton +1
Gaussian process (GP) regression is a flexible, nonparametric approach to regression that naturally quantifies uncertainty. In many applications, the number of responses and covari…
Sub-dimensional Mardia measures of multivariate skewness and kurtosis
Joydeep Chowdhury, Subhajit Dutta, Reinaldo B. Arellano-Valle +1
The Mardia measures of multivariate skewness and kurtosis summarize the respective characteristics of a multivariate distribution with two numbers. However, these measures do not r…
Sparse Functional Boxplots for Multivariate Curves
Zhuo Qu, Marc G. Genton
This paper introduces the sparse functional boxplot and the intensity sparse functional boxplot as practical exploratory tools. Besides being available for complete functional data…
Tractable Bayes of Skew-Elliptical Link Models for Correlated Binary Data
Zhongwei Zhang, Reinaldo B. Arellano-Valle, Marc G. Genton +1
Correlated binary response data with covariates are ubiquitous in longitudinal or spatial studies. Among the existing statistical models the most well-known one for this type of da…
A Generalized Heckman Model With Varying Sample Selection Bias and Dispersion Parameters
Fernando de S. Bastos, Wagner Barreto-Souza, Marc G. Genton
Many proposals have emerged as alternatives to the Heckman selection model, mainly to address the non-robustness of its normal assumption. The 2001 Medical Expenditure Panel Survey…
Are You All Normal? It Depends!
Wanfang Chen, Marc G. Genton
The assumption of normality has underlain much of the development of statistics, including spatial statistics, and many tests have been proposed. In this work, we focus on the mult…
Test and Visualization of Covariance Properties for Multivariate Spatio-Temporal Random Fields
Huang Huang, Ying Sun, Marc G. Genton
The prevalence of multivariate space-time data collected from monitoring networks and satellites, or generated from numerical models, has brought much attention to multivariate spa…