Bayesian Hierarchical Varying-Sparsity Regression Models with Application to Cancer Proteogenomics

DOI10.1080/01621459.2018.1434529zbMath1462.62676OpenAlexW2786059902WikidataQ92616665 ScholiaQ92616665MaRDI QIDQ5229891

Veerabhadran Baladandayuthapani, Min Jin Ha, Rehan Akbani, Yang Ni, Francesco C. Stingo

Publication date: 19 August 2019

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc6552682

zbMATH Keywords

thresholdtumor heterogeneityprecision medicinep-splinesprognostic biomarker

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) General nonlinear regression (62J02)

Related Items (2)

Drug sensitivity prediction with normal inverse Gaussian shrinkage informed by external data ⋮ Subject-specific Dirichlet-multinomial regression for multi-district microbiota data analysis

Uses Software

• SemiPar
• shiny
• RSEM
• TCGA-assembler
• MapSplice

Cites Work

• Unnamed Item
• Bayes and empirical-Bayes multiplicity adjustment in the variable-selection problem
• Optimal predictive model selection.
• Semiparametric Regression
• Spike-and-Slab Priors for Function Selection in Structured Additive Regression Models
• Nonparametric Independence Screening in Sparse Ultra-High-Dimensional Varying Coefficient Models
• Variable Selection in Nonparametric Varying-Coefficient Models for Analysis of Repeated Measurements
• Doubly Penalized Buckley–James Method for Survival Data with High‐Dimensional Covariates

This page was built for publication: Bayesian Hierarchical Varying-Sparsity Regression Models with Application to Cancer Proteogenomics