Predicting times to event based on vine copula models

DOI10.1016/j.csda.2022.107546OpenAlexW3211966483WikidataQ114191809 ScholiaQ114191809MaRDI QIDQ2674484

Shenyi Pan, Joe, Harry

Publication date: 14 September 2022

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2111.07179

zbMATH Keywords

survival analysisprediction intervalconditional quantilesvine copulatime-to-event analysiscopula regression

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (1)

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Uses Software

• survival
• VineCopula
• CopulaModel

Cites Work

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• Unnamed Item
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• Random survival forests
• D-vine copula based quantile regression
• survival
• The polyserial correlation coefficient
• Vine copula based likelihood estimation of dependence patterns in multivariate event time data
• Analyzing dependent data with vine copulas. A practical guide with R
• Vines -- a new graphical model for dependent random variables.
• Survival analysis. Techniques for censored and truncated data.
• Prediction based on conditional distributions of vine copulas
• Probability density decomposition for conditionally dependent random variables modeled by vines
• Dependence Modeling with Copulas
• Survival ensembles
• A Bayesian joint model of recurrent events and a terminal event
• Dependence modeling for recurrent event times subject to right‐censoring with D‐vine copulas
• Strictly Proper Scoring Rules, Prediction, and Estimation

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