4 citations · 7 across the 10 of their papers we have counts for
16 papers
JASPER: Joint Bayesian Analysis of Spatial Expression via Regression
Pritam Dey, Rajarshi Guhaniyogi, Yang Ni +1
Spatially resolved transcriptomics is a fast-developing set of technologies that enables the measurement of localized gene expression across spatial locations in a sample. Detectin…
Uncertainty-Aware Neural Multivariate Geostatistics
Yeseul Jeon, Aaron Scheffler, Rajarshi Guhaniyogi
We propose Deep Neural Coregionalization, a scalable framework for uncertainty-aware multivariate geostatistics. DNC models multivariate spatial effects through spatially varying l…
Supervised Learning of Functional Outcomes with Predictors at Different Scales: A Functional Gaussian Process Approach
R. Jacob Andros, Rajarshi Guhaniyogi, Devin Francom +1
The analysis of complex computer simulations, often involving functional data, presents unique statistical challenges. Conventional regression methods, such as function-on-function…
Mapping Drivers of Greenness: Spatial Variable Selection for MODIS Vegetation Indices
Qishi Zhan, Cheng-Han Yu, Yuchi Chen +2
Understanding how environmental drivers relate to vegetation condition motivates spatially varying regression models, but estimating a separate coefficient surface for every predic…
Adaptive Divide and Conquer with Two Rounds of Communication
Niladri Kal, Botond Szabó, Rajarshi Guhaniyogi +2
We introduce a two-round adaptive communication strategy that enables rate-optimal estimation in the white noise model without requiring prior knowledge of the underlying smoothnes…
Bayesian Data Sketching for Varying Coefficient Regression Models
Rajarshi Guhaniyogi, Laura Baracaldo, Sudipto Banerjee
Varying coefficient models are popular for estimating nonlinear regression functions in functional data models. Their Bayesian variants have received limited attention in large dat…