30 citations · 62 across the 10 of their papers we have counts for
12 papers
High Performance Multivariate Geospatial Statistics on Manycore Systems
Mary Lai O. Salvaña, Sameh Abdulah, Huang Huang +4
Modeling and inferring spatial relationships and predicting missing values of environmental data are some of the main tasks of geospatial statisticians. These routine tasks are acc…
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…
Clustering Brain Signals: A Robust Approach Using Functional Data Ranking
Tianbo Chen, Ying Sun, Carolina Euan +1
In this paper, we analyze electroencephalograms (EEG) which are recordings of brain electrical activity. We develop new clustering methods for identifying synchronized brain region…
DeepKriging: Spatially Dependent Deep Neural Networks for Spatial Prediction
Wanfang Chen, Yuxiao Li, Brian J Reich +1
In spatial statistics, a common objective is to predict values of a spatial process at unobserved locations by exploiting spatial dependence. Kriging provides the best linear unbia…
Geostatistical Modeling and Prediction Using Mixed-Precision Tile Cholesky Factorization
Sameh Abdulah, Hatem Ltaief, Ying Sun +2
Geostatistics represents one of the most challenging classes of scientific applications due to the desire to incorporate an ever increasing number of geospatial locations to accura…
A Multi-Site Stochastic Weather Generator for High-Frequency Precipitation Using Censored Skew-Symmetric Distribution
Yuxiao Li, Ying Sun
Stochastic weather generators (SWGs) are digital twins of complex weather processes and widely used in agriculture and urban design. Due to improved measuring instruments, an accur…
Large-scale Environmental Data Science with ExaGeoStatR
Sameh Abdulah, Yuxiao Li, Jian Cao +4
Parallel computing in Gaussian process calculations becomes necessary for avoiding computational and memory restrictions associated with large-scale environmental data science appl…
Parallel Approximation of the Maximum Likelihood Estimation for the Prediction of Large-Scale Geostatistics Simulations
Sameh Abdulah, Hatem Ltaief, Ying Sun +2
Maximum likelihood estimation is an important statistical technique for estimating missing data, for example in climate and environmental applications, which are usually large and…
Likelihood Approximation With Hierarchical Matrices For Large Spatial Datasets
Alexander Litvinenko, Ying Sun, Marc G. Genton +1
We use available measurements to estimate the unknown parameters (variance, smoothness parameter, and covariance length) of a covariance function by maximizing the joint Gaussian l…
Visualization and Assessment of Spatio-temporal Covariance Properties
Huang Huang, Ying Sun
Spatio-temporal covariances are important for describing the spatio-temporal variability of underlying random processes in geostatistical data. For second-order stationary processe…
Total Variation Depth for Functional Data
Huang Huang, Ying Sun
There has been extensive work on data depth-based methods for robust multivariate data analysis. Recent developments have moved to infinite-dimensional objects such as functional d…
A stochastic space-time model for intermittent precipitation occurrences
Ying Sun, Michael L. Stein
Modeling a precipitation field is challenging due to its intermittent and highly scale-dependent nature. Motivated by the features of high-frequency precipitation data from a netwo…