41 citations · 41 across the 16 of their papers we have counts for
18 papers
Gaussian Process Decorrelation for Spatiotemporal Deep Learning-Based Snow Water Equivalent Prediction
Colin Fenster, Adrienne Marshall, Soutir Bandyopadhyay +1
In the Western United States, snowmelt is essential to the agricultural industry in addition to being a key source of municipal drinking water. Consequently, accurate snowpack fore…
Spatial function-on-function quantile regression
Eylul Fidan, Ufuk Beyaztas, Soutir Bandyopadhyay
This paper introduces a novel penalized spatial function-on-function quantile regression framework for analyzing spatially indexed functional data, bridging a critical gap between…
Local Variable and Neighborhood Selection for Firearm Fatality in the Southeast USA
Debjoy Thakur, Lingyuan Zhao, Soutir Bandyopadhyay
A major public health concern in the United States (US) is gun-related deaths. The number of gun injuries largely varies spatially because of county-wise heterogeneity of race, sex…
Cumulative Logit Ordinal Regression with Proportional Odds under Nonignorable Missing Response -- Application to Phase III Trial
Arnab Kumar Maity, Huaming Tan, Vivek Pradhan +1
Missing data are inevitable in clinical trials, and trials that produce categorical ordinal responses are not exempted from this. Typically, missing values in the data occur due to…
Modeling Spatial Extremes using Non-Gaussian Spatial Autoregressive Models via Convolutional Neural Networks
Sweta Rai, Douglas W. Nychka, Soutir Bandyopadhyay
Data derived from remote sensing or numerical simulations often have a regular gridded structure and are large in volume, making it challenging to find accurate spatial models that…
Frequency Domain Resampling for Gridded Spatial Data
Souvick Bera, Daniel J. Nordman, Soutir Bandyopadhyay
In frequency domain analysis for spatial data, spectral averages based on the periodogram often play an important role in understanding spatial covariance structure, but also have…
A Statistical Framework for District Energy Long-term Electric Load Forecasting
Emily Royal, Soutir Bandyopadhyay, Alexandra Newman +2
An accurate forecast of electric demand is essential for the optimal design of a generation system. For district installations, the projected lifespan may extend one or two decades…
Adapting Quantile Mapping to Bias Correct Solar Radiation Data
Maggie D. Bailey, Douglas W. Nychka, Manajit Sengupta +1
Bias correction is a common pre-processing step applied to climate model data before it is used for further analysis. This article introduces an efficient adaptation of a well-esta…
Temporal and spatial downscaling for solar radiation
Maggie Bailey, Doug Nychka, Manajit Sengupta +2
Global and regional climate model projections are useful for gauging future patterns of climate variables, including solar radiation, but data from these models is often too coarse…
Fast parameter estimation of Generalized Extreme Value distribution using Neural Networks
Sweta Rai, Alexis Hoffman, Soumendra Lahiri +3
The heavy-tailed behavior of the generalized extreme-value distribution makes it a popular choice for modeling extreme events such as floods, droughts, heatwaves, wildfires, etc. H…
Regridding Uncertainty for Statistical Downscaling of Solar Radiation
Maggie Bailey, Douglas Nychka, Manajit Sengupta +3
Initial steps in statistical downscaling involve being able to compare observed data from regional climate models (RCMs). This prediction requires (1) regridding RCM output from th…
Adapting conditional simulation using circulant embedding for irregularly spaced spatial data
Maggie D. Bailey, Soutir Bandyopadhyay, Douglas W. Nychka
Computing an ensemble of random fields using conditional simulation is an ideal method for retrieving accurate estimates of a field conditioned on available data and for quantifyin…
Robust Density Power Divergence Estimates for Panel Data Models
Abhijit Mandal, Beste Hamiye Beyaztas, Soutir Bandyopadhyay
The panel data regression models have become one of the most widely applied statistical approaches in different fields of research, including social, behavioral, environmental scie…
A robust specification test in linear panel data models
Beste Hamiye Beyaztas, Soutir Bandyopadhyay, Abhijit Mandal
The presence of outlying observations may adversely affect statistical testing procedures that result in unstable test statistics and unreliable inferences depending on the distort…
Data Driven Robust Estimation Methods for Fixed Effects Panel Data Models
Beste Hamiye Beyaztas, Soutir Bandyopadhyay
The panel data regression models have gained increasing attention in different areas of research including but not limited to econometrics, environmental sciences, epidemiology, be…
Robust Estimation for Linear Panel Data Models
Beste Hamiye Beyaztas, Soutir Bandyopadhyay
In different fields of applications including, but not limited to, behavioral, environmental, medical sciences and econometrics, the use of panel data regression models has become…
Rapid Numerical Approximation Method for Integrated Covariance Functions Over Irregular Data Regions
Peter Simonson, Douglas Nychka, Soutir Bandyopadhyay
In many practical applications, spatial data are often collected at areal levels (i.e., block data) and the inferences and predictions about the variable at points or blocks differ…
A frequency domain empirical likelihood method for irregularly spaced spatial data
Soutir Bandyopadhyay, Soumendra N. Lahiri, Daniel J. Nordman
This paper develops empirical likelihood methodology for irregularly spaced spatial data in the frequency domain. Unlike the frequency domain empirical likelihood (FDEL) methodolog…