Bayesian survival trees for clustered observations, applied to tooth prognosis

DOI10.1002/sam.11215OpenAlexW2119385341MaRDI QIDQ4969929

Richard A. Levine, Juanjuan Fan, Martha E. Nunn, Xiaogang Su

Publication date: 14 October 2020

Published in: Statistical Analysis and Data Mining: The ASA Data Science Journal (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1002/sam.11215

zbMATH Keywords

frailty modelrandom forestclassification and regression trees (CARTs)clustered/correlated failure timesdental applicationmetropolis-Hastings (MCMC) sampler

Mathematics Subject Classification ID

Statistics (62-XX) Computer science (68-XX)

Uses Software

• randomSurvivalForest

Cites Work

• Parallel hierarchical sampling: a general-purpose interacting Markov chains Monte Carlo algorithm
• Bayesian Weibull tree models for survival analysis of clinico-genomic data
• Multivariate exponential survival trees and their application to tooth prognosis
• Stratification by stepwise regression, correspondence analysis and recursive partition: A comparison of three methods of analysis for survival data with covariates
• Bayesian Treed Gaussian Process Models With an Application to Computer Modeling
• Multivariate Survival Trees: A Maximum Likelihood Approach Based on Frailty Models
• Accurate Approximations for Posterior Moments and Marginal Densities
• A Bayesian CART algorithm
• High-Dimensional Variable Selection for Survival Data
• Random forests
• Bayesian treed models

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