research
∙
08/28/2023
Which Parameterization of the Matérn Covariance Function?
The Matérn family of covariance functions is currently the most popularl...
share
research
∙
06/20/2023
Efficient Large-scale Nonstationary Spatial Covariance Function Estimation Using Convolutional Neural Networks
Spatial processes observed in various fields, such as climate and enviro...
research
∙
04/20/2023
On the non-identifiability of unified skew-normal distributions
In this note, we investigate the non-identifiability of the multivariate...
research
∙
03/08/2023
Goodness-of-fit tests for multivariate skewed distributions based on the characteristic function
We employ a general Monte Carlo method to test composite hypotheses of g...
research
∙
11/28/2022
Global Depths for Irregularly Observed Multivariate Functional Data
Two frameworks for multivariate functional depth based on multivariate d...
research
∙
08/05/2022
Nonseparable Space-Time Stationary Covariance Functions on Networks cross Time
The advent of data science has provided an increasing number of challeng...
research
∙
07/26/2022
Large-Scale Low-Rank Gaussian Process Prediction with Support Points
Low-rank approximation is a popular strategy to tackle the "big n proble...
research
∙
07/26/2022
Multivariate Functional Outlier Detection using the FastMUOD Indices
We present definitions and properties of the fast massive unsupervised o...
research
∙
05/22/2022
Spatio-Temporal Cross-Covariance Functions under the Lagrangian Framework with Multiple Advections
When analyzing the spatio-temporal dependence in most environmental and ...
research
∙
04/26/2022
Robust Two-Layer Partition Clustering of Sparse Multivariate Functional Data
In this work, a novel elastic time distance for sparse multivariate func...
research
∙
02/25/2022
Scalable Gaussian-process regression and variable selection using Vecchia approximations
Gaussian process (GP) regression is a flexible, nonparametric approach t...
research
∙
12/24/2021
Sensitivity Analysis of Wind Energy Resources with Bayesian non-Gaussian and nonstationary Functional ANOVA
The transition from non-renewable to renewable energies represents a glo...
research
∙
11/29/2021
Sub-Dimensional Mardia Measures of Multivariate Skewness and Kurtosis
Mardia's measures of multivariate skewness and kurtosis summarize the re...
research
∙
03/14/2021
Sparse Functional Boxplots for Multivariate Curves
This paper introduces the sparse functional boxplot and the intensity sp...
research
∙
02/01/2021
Forecasting High-Frequency Spatio-Temporal Wind Power with Dimensionally Reduced Echo State Networks
Fast and accurate hourly forecasts of wind speed and power are crucial i...
research
∙
01/06/2021
Tractable Bayes of Skew-Elliptical Link Models for Correlated Binary Data
Correlated binary response data with covariates are ubiquitous in longit...
research
∙
12/03/2020
A Generalized Heckman Model With Varying Sample Selection Bias and Dispersion Parameters
Many proposals have emerged as alternatives to the Heckman selection mod...
research
∙
11/02/2020
Assessing the Reliability of Wind Power Operations under a Changing Climate with a Non-Gaussian Distributional Adjustment
Facing increasing societal and economic pressure, many countries have es...
research
∙
09/03/2020
Scalable computation of predictive probabilities in probit models with Gaussian process priors
Predictive models for binary data are fundamental in various fields, and...
research
∙
08/25/2020
Are You All Normal? It Depends!
The assumption of normality has underlain much of the development of sta...
research
∙
08/09/2020
Visualization of Covariance Structures for Multivariate Spatio-Temporal Random Fields
The prevalence of multivariate space-time data collected from monitoring...
research
∙
08/03/2020
High Performance Multivariate Spatial Modeling for Geostatistical Data on Manycore Systems
Modeling and inferring spatial relationships and predicting missing valu...
research
∙
06/21/2020
Conditional Normal Extreme-Value Copulas
We propose a new class of extreme-value copulas which are extreme-value ...
research
∙
03/25/2020
Exploiting Low Rank Covariance Structures for Computing High-Dimensional Normal and Student-t Probabilities
We present a preconditioned Monte Carlo method for computing high-dimens...
research
∙
03/10/2020
A Pairwise Hotelling Method for Testing High-Dimensional Mean Vectors
For high-dimensional small sample size data, Hotelling's T2 test is not ...
research
∙
01/14/2020
Nonparametric Trend Estimation in Functional Time Series with Application to Annual Mortality Rates
Here, we address the problem of trend estimation for functional time ser...
research
∙
01/07/2020
Vector Autoregressive Models with Spatially Structured Coefficients for Time Series on a Spatial Grid
We propose a parsimonious spatiotemporal model for time series data on a...
research
∙
01/05/2020
Recent Developments in Complex and Spatially Correlated Functional Data
As high-dimensional and high-frequency data are being collected on a lar...
research
∙
11/11/2019
Efficiency Assessment of Approximated Spatial Predictions for Large Datasets
Due to the well-known computational showstopper of the exact Maximum Lik...
research
∙
09/13/2019
Generalized Records for Functional Time Series with Application to Unit Root Tests
A generalization of the definition of records to functional data is prop...
research
∙
07/23/2019
ExaGeoStatR: A Package for Large-Scale Geostatistics in R
Parallel computing in Gaussian process calculation becomes a necessity f...
research
∙
07/16/2019
Improving Bayesian Local Spatial Models in Large Data Sets
Environmental processes resolved at a sufficiently small scale in space ...
research
∙
04/24/2019
Trajectory Functional Boxplots
With the development of data-monitoring techniques in various fields of ...
research
∙
12/21/2018
Spatial Blind Source Separation
Recently a blind source separation model was suggested for multivariate ...
research
∙
08/16/2018
Functional Outlier Detection and Taxonomy by Sequential Transformations
Functional data analysis can be seriously impaired by abnormal observati...
research
∙
04/24/2018
Parallel Approximation of the Maximum Likelihood Estimation for the Prediction of Large-Scale Geostatistics Simulations
Maximum likelihood estimation is an important statistical technique for ...
research
∙
04/24/2018
Tile Low-Rank Approximation of Large-Scale Maximum Likelihood Estimation on Manycore Architectures
Maximum likelihood estimation is an important statistical technique for ...
research
∙
03/18/2018
Bayesian Modeling of Air Pollution Extremes Using Nested Multivariate Max-Stable Processes
Capturing the potentially strong dependence among the peak concentration...
research
∙
12/06/2017
A Multi-Resolution Spatial Model for Large Datasets Based on the Skew-t Distribution
Large, non-Gaussian spatial datasets pose a considerable modeling challe...
research
∙
11/10/2017
A Stochastic Generator of Global Monthly Wind Energy with Tukey g-and-h Autoregressive Processes
Quantifying the uncertainty of wind energy potential from climate models...
research
∙
09/08/2017
Likelihood Approximation With Hierarchical Matrices For Large Spatial Datasets
We use available measurements to estimate the unknown parameters (varian...
research
∙
04/09/2017
