3 citations · 7 across the 7 of their papers we have counts for
9 papers
Nonstationary Spatial Modeling of Massive Global Satellite Data
Huang Huang, Lewis R. Blake, Matthias Katzfuss +1
Earth-observing satellite instruments obtain a massive number of observations every day. For example, tens of millions of sea surface temperature (SST) observations on a global sca…
Forecasting High-Frequency Spatio-Temporal Wind Power with Dimensionally Reduced Echo State Networks
Huang Huang, Stefano Castruccio, Marc G. Genton
Fast and accurate hourly forecasts of wind speed and power are crucial in quantifying and planning the energy budget in the electric grid. Modeling wind at a high resolution brings…
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…
Combining interdependent climate model outputs in CMIP5: A spatial Bayesian approach
Huang Huang, Dorit Hammerling, Bo Li +1
Projections of future climate change rely heavily on climate models, and combining climate models through a multi-model ensemble is both more accurate than a single climate model a…
Pushing the Limit: A Hybrid Parallel Implementation of the Multi-resolution Approximation for Massive Data
Huang Huang, Lewis R. Blake, Dorit M. Hammerling
The multi-resolution approximation (MRA) of Gaussian processes was recently proposed to conduct likelihood-based inference for massive spatial data sets. An advantage of the method…
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…
Hierarchical low rank approximation of likelihoods for large spatial datasets
Huang Huang, Ying Sun
Datasets in the fields of climate and environment are often very large and irregularly spaced. To model such datasets, the widely used Gaussian process models in spatial statis- ti…