1 citations · 2 across the 3 of their papers we have counts for
8 papers
Hierarchical Multivariate Directed Acyclic Graph Auto-Regressive (MDAGAR) models for spatial diseases mapping
Leiwen Gao, Abhirup Datta, Sudipto Banerjee
Disease mapping is an important statistical tool used by epidemiologists to assess geographic variation in disease rates and identify lurking environmental risk factors from spatia…
A Transformation-free Linear Regression for Compositional Outcomes and Predictors
Jacob Fiksel, Scott Zeger, Abhirup Datta
Compositional data are common in many fields, both as outcomes and predictor variables. The inventory of models for the case when both the outcome and predictor variables are compo…
spNNGP R package for Nearest Neighbor Gaussian Process models
Andrew O. Finley, Abhirup Datta, Sudipto Banerjee
This paper describes and illustrates functionality of the spNNGP R package. The package provides a suite of spatial regression models for Gaussian and non-Gaussian point-referenced…
Generalized Bayes Quantification Learning under Dataset Shift
Jacob Fiksel, Abhirup Datta, Agbessi Amouzou +1
Quantification learning is the task of prevalence estimation for a test population using predictions from a classifier trained on a different population. Quantification methods ass…
Spatial Modeling for Correlated Cancers Using Bivariate Directed Graphs
Leiwen Gao, Sudipto Banerjee, Abhirup Datta
Disease maps are an important tool in cancer epidemiology used for the analysis of geographical variations in disease rates and the investigation of environmental risk factors unde…
Percentile-Based Residuals for Model Assessment
Sophie Bérubé, Abhirup Datta, Qingfeng Li +2
Residuals are a key component of diagnosing model fit. The usual practice is to compute standardized residuals using expected values and standard deviations of the observed data, t…