The Annals of Applied Statistics, Vol. 15, No. 1 (March 2021), pp. 391-411 (21 pages) Gene expression deconvolution is a powerful tool for exploring the microenvironment of complex tissues comprised ...
An important problem in shape analysis is to match configurations of points in space after filtering out some geometrical transformation. In this paper we introduce hierarchical models for such tasks, ...
A Bayesian hierarchical model was developed to estimate the parameters in a physiologically based pharmacokinetic (PBPK) model for chloroform using prior information and biomarker data from different ...
BhGLM is a freely available R package that implements Bayesian hierarchical modeling for high-dimensional clinical and genomic data. It consists of functions for setting up various Bayesian ...
We adapt a semi-Bayesian hierarchical modeling framework to jointly characterize the space–time variability of seasonal precipitation totals and precipitation extremes across the Northern Great Plains ...
We present a spatial Bayesian hierarchical model for seasonal extreme precipitation. At the first level of hierarchy, the seasonal maximum precipitation (i.e. block maxima) at any location is assumed ...
Interventions targeting drinking water safety and responsible dog management could reduce echinococcosis incidence in China.
This study compares two different techniques in a time series small area application: state space models estimated with the Kalman filter with a frequentist approach to hyperparameter estimation, and ...
Flood damage processes are complex and vary between events and regions. State‐of‐the‐art flood loss models are often developed on the basis of empirical damage data from specific case studies and do ...
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