ArticleStatistical applications in genetics and molecular biology2012
Hierarchical shrinkage priors and model fitting for high-dimensional generalized linear models.
Article in Statistical applications in genetics and molecular biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- Uncertainty-aware traction force microscopy.PLoS computational biology · 2025Article
- HBI: a hierarchical Bayesian interaction model to estimate cell-type-specific methylation quantitative trait loci incorporating priors from cell-sorted bisulfite sequencing data.Genome biology · 2024Article
- Revisiting the complex time-varying effect of non-pharmaceutical interventions on COVID-19 transmission in the United States.Frontiers in public health · 2024Article
- Spike-and-slab least absolute shrinkage and selection operator generalized additive models and scalable algorithms for high-dimensional data analysis.Statistics in medicine · 2022Article
- Article
- Bayesian Hyper-LASSO Classification for Feature Selection with Application to Endometrial Cancer RNA-seq Data.Scientific reports · 2020Article
- BhGLM: Bayesian hierarchical GLMs and survival models, with applications to genomics and epidemiology.Bioinformatics (Oxford, England) · 2019Article
- Bayesian hierarchical negative binomial models for multivariable analyses with applications to human microbiome count data.PloS one · 2019Article
- Inclusion of biological knowledge in a Bayesian shrinkage model for joint estimation of SNP effects.Genetic epidemiology · 2017Article
- Pathway-Structured Predictive Model for Cancer Survival Prediction: A Two-Stage Approach.Genetics · 2017Article
- A two-stage approach for combining gene expression and mutation with clinical data improves survival prediction in myelodysplastic syndromes and ovarian cancer.Journal of bioinformatics and genomics · 2016Article
- Multiple comparisons in genetic association studies: a hierarchical modeling approach.Statistical applications in genetics and molecular biology · 2014Article
- Bayesian Methods for High Dimensional Linear Models.Journal of biometrics & biostatistics · 2013Article
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2 authors.
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Abstract
Abstract Genetic and other scientific studies routinely generate very many predictor variables, which can be naturally grouped, with predictors in the same groups being highly correlated. It is desirable to incorporate the hierarchical structure of the predictor variables into generalized linear models for simultaneous variable selection and coefficient estimation. We propose two prior distributions: hierarchical Cauchy and double-exponential distributions, on coefficients in generalized linear models. The hierarchical priors include both variable-specific and group-specific tuning parameters, thereby not only adopting different shrinkage for different coefficients and different groups but also providing a way to pool the information within groups. We fit generalized linear models with the proposed hierarchical priors by incorporating flexible expectation-maximization (EM) algorithms into the standard iteratively weighted least squares as implemented in the general statistical package R. The methods are illustrated with data from an experiment to identify genetic polymorphisms for survival of mice following infection with Listeria monocytogenes. The performance of the proposed procedures is further assessed via simulation studies. The methods are implemented in a freely available R package BhGLM (http://www.ssg.uab.edu/bhglm/).
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