Bayesian analysis of survival on multiple time scales
Copyright policy )Bayesian analysis of survival on multiple time scales
AbstractWe propose a Bayesian approach to the analysis of survival data on multiple time scales. Non‐parametric modelling of variation of rates with more than one time scale is achieved using priors which specifysmoothvariation. Computations are conveniently carried out using Gibbs sampling. We discuss the extension of the method to Bayesian forecasting of rates. Numerical experience of two examples is described.
- University of Manchester United Kingdom
- University of Pavia Italy
- University of Manchester United Kingdom
Adult, Aged, 80 and over, Male, Risk, Likelihood Functions, Lung Neoplasms, Models, Statistical, Adolescent, Cox's partial likelihood, Age Factors, Bayes Theorem, Middle Aged, Survival Analysis, Bayesian forecasting, Cohort Studies, Survival Rate, Postoperative Complications, non-parametric modelling, Heart Transplantation, Humans, Female, Aged, Proportional Hazards Models
Adult, Aged, 80 and over, Male, Risk, Likelihood Functions, Lung Neoplasms, Models, Statistical, Adolescent, Cox's partial likelihood, Age Factors, Bayes Theorem, Middle Aged, Survival Analysis, Bayesian forecasting, Cohort Studies, Survival Rate, Postoperative Complications, non-parametric modelling, Heart Transplantation, Humans, Female, Aged, Proportional Hazards Models
selected citations These citations are derived from selected sources.This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).99 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 1% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
Citations provided by BIP!Popularity provided by BIP!