This is a preview. Log in through your library . Abstract Log-linear models have been shown to be useful for smoothing contingency tables when categorical outcomes are subject to nonignorable ...

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 ...

A strength of Bayesian hierarchical modeling is that it allows inclusion of diverse sources of information, but I’m sure other methods could do fine also, if set up appropriately. P.P.S.

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 ...

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 ...

A separate model is developed for each season to capture the unique spatial features prevalent in the precipitation field. This modeling framework offers a flexible approach to incorporate covariates ...

Results As demonstrated by the simulation results in each borrowing scenario, the Bayesian hierarchical model and its extensions are more appropriate for concurrent borrowing. The simulation results ...

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 ...

Predisposition to a disease is usually caused by cumulative effects of a multitude of exposures and lifestyle factors in combination with individual susceptibility. Failure to include all relevant ...