Modeling pedestrian-injury severities in pedestrian-vehicle crashes considering spatiotemporal patterns: Insights from different hierarchical Bayesian random-effects models
Sign Up to like & getrecommendations! Published in 2020 at "Analytic Methods in Accident Research"
DOI: 10.1016/j.amar.2020.100137
Abstract: Abstract To systematically account for the spatiotemporal features and unobserved heterogeneity within pedestrian-vehicle crashes, this paper employs the spatiotemporal analysis and hierarchical Bayesian random-effects models to explore the factors contributing to pedestrian-injury severities of pedestrian-vehicle… read more here.
Keywords: bayesian random; vehicle; random effects; hierarchical bayesian ... See more keywords
BREM-SC: a bayesian random effects mixture model for joint clustering single cell multi-omics data
Sign Up to like & getrecommendations! Published in 2020 at "Nucleic Acids Research"
DOI: 10.1093/nar/gkaa314
Abstract: Abstract Droplet-based single cell transcriptome sequencing (scRNA-seq) technology, largely represented by the 10× Genomics Chromium system, is able to measure the gene expression from tens of thousands of single cells simultaneously. More recently, coupled with… read more here.
Keywords: single cell; bayesian random; effects mixture; model ... See more keywords
Bayesian Random-Effects Meta-Analysis Using the bayesmeta R Package
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Statistical Software"
DOI: 10.18637/jss.v093.i06
Abstract: The random-effects or normal-normal hierarchical model is commonly utilized in a wide range of meta-analysis applications. A Bayesian approach to inference is very attractive in this context, especially when a meta-analysis is based only on… read more here.
Keywords: meta analysis; random effects; bayesian random; bayesmeta package ... See more keywords