Spike-and-slab type variable selection in the Cox proportional hazards model for high-dimensional features

DOI10.1080/02664763.2021.1893285OpenAlexW3134304070MaRDI QIDQ5092986

Mihye Ahn, Hojin Yang, Ryan Wu

Publication date: 26 July 2022

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02664763.2021.1893285

zbMATH Keywords

Markov chain Monte CarloBayesian modelingscore functionlung adenocarcinomalatent indicatorstochastic variable search

Mathematics Subject Classification ID

Linear regression; mixed models (62J05) Estimation in survival analysis and censored data (62N02) Applications of statistics (62Pxx)

Uses Software

• coxphMIC

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Sparse estimation of Cox proportional hazards models via approximated information criteria
• High-dimensional Cox regression analysis in genetic studies with censored survival outcomes
• Bayesian adaptive Lasso
• Spike and slab variable selection: frequentist and Bayesian strategies
• BAYESIAN HYPER-LASSOS WITH NON-CONVEX PENALIZATION
• MILFM: Multiple index latent factor model based on high-dimensional features
• Quantile Function on Scalar Regression Analysis for Distributional Data
• A Selective Overview of Variable Selection in High Dimensional Feature Space (Invited Review Article)
• The Bayesian Lasso
• Bayesian Variable Selection in Linear Regression
• Model Selection and Accounting for Model Uncertainty in Graphical Models Using Occam's Window
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Adaptive Lasso for Cox's proportional hazards model
• Clustering high dimension, low sample size data using the maximal data piling distance
• Bayesian survival analysis

This page was built for publication: Spike-and-slab type variable selection in the Cox proportional hazards model for high-dimensional features