5 citations · 12 across the 10 of their papers we have counts for
10 papers
A Bayesian Framework for Built-in Input Dimension Reduction for Gaussian Process Modeling
Eric Herrison Gyamfi, Emily L. Kang, Bledar A. Konomi +1
Gaussian process (GP) modeling is widely used in computational science and engineering. However, fitting a GP to high-dimensional inputs remains challenging due to the curse of dim…
Recursive Nearest Neighbor Co-Kriging Models for Big Multiple Fidelity Spatial Data Sets
Si Cheng, Bledar A. Konomi, Georgos Karagiannis +1
Big datasets are gathered daily from different remote sensing platforms. Recently, statistical co-kriging models, with the help of scalable techniques, have been able to combine su…
Bayesian Latent Variable Co-kriging Model in Remote Sensing for Observations with Quality Flagged
Bledar A. Konomi, Emily L. Kang, Ayat Almomani +1
Remote sensing data products often include quality flags that inform users whether the associated observations are of good, acceptable or unreliable qualities. However, such inform…
Statistical Downscaling of Model Projections with Multivariate Basis Graphical Lasso
Ayesha Ekanayaka, Emily Kang, Peter Kalmus +1
We describe an improved statistical downscaling method for Earth science applications using multivariate Basis Graphical Lasso (BGL). We demonstrate our method using a case study o…
Hierarchical Bayesian Nearest Neighbor Co-Kriging Gaussian Process Models; An Application to Intersatellite Calibration
Si Cheng, Bledar A. Konomi, Jessica L. Matthews +2
Recent advancements in remote sensing technology and the increasing size of satellite constellations allows massive geophysical information to be gathered daily on a global scale b…
Computer Model Emulation with High-Dimensional Functional Output in Large-Scale Observing System Uncertainty Experiments
Pulong Ma, Anirban Mondal, Bledar Konomi +3
Observing system uncertainty experiments (OSUEs) have been recently proposed as a cost-effective way to perform probabilistic assessment of retrievals for NASA's Orbiting Carbon Ob…
Spatio-Temporal Data Fusion for Massive Sea Surface Temperature Data from MODIS and AMSR-E Instruments
Pulong Ma, Emily L. Kang
Remote sensing data have been widely used to study various geophysical processes. With the advances in remote-sensing technology, massive amount of remote sensing data are collecte…
An Additive Approximate Gaussian Process Model for Large Spatio-Temporal Data
Pulong Ma, Bledar A. Konomi, Emily L. Kang
Motivated by a large ground-level ozone dataset, we propose a new computationally efficient additive approximate Gaussian process. The proposed method incorporates a computational-…
Spatial Statistical Downscaling for Constructing High-Resolution Nature Runs in Global Observing System Simulation Experiments
Pulong Ma, Emily L. Kang, Amy Braverman +1
Observing system simulation experiments (OSSEs) have been widely used as a rigorous and cost-effective way to guide development of new observing systems, and to evaluate the perfor…
A Fused Gaussian Process Model for Very Large Spatial Data
Pulong Ma, Emily L. Kang
With the development of new remote sensing technology, large or even massive spatial datasets covering the globe become available. Statistical analysis of such data is challenging.…