1 citations · 2 across the 4 of their papers we have counts for
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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…
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…
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…
Regularized Bayesian transfer learning for population level etiological distributions
Abhirup Datta, Jacob Fiksel, Agbessi Amouzou +1
Computer-coded verbal autopsy (CCVA) algorithms predict cause of death from high-dimensional family questionnaire data (verbal autopsies) of a deceased individual. CCVA algorithms…
Practical Bayesian Modeling and Inference for Massive Spatial Datasets On Modest Computing Environments
Lu Zhang, Abhirup Datta, Sudipto Banerjee
With continued advances in Geographic Information Systems and related computational technologies, statisticians are often required to analyze very large spatial datasets. This has…