Reinforced urns and the subdistribution beta-Stacy process prior for competing risks analysis

11/29/2018
by   Andrea Arfé, et al.
0

In this paper we introduce the subdistribution beta-Stacy process, a novel Bayesian nonparametric process prior for subdistribution functions useful for the analysis of competing risks data. In particular, we i) characterize this process from a predictive perspective by means of an urn model with reinforcement, ii) show that it is conjugate with respect to right-censored data, and iii) highlight its relations with other prior processes for competing risks data. Additionally, we consider the subdistribution beta-Stacy process prior in a nonparametric regression model for competing risks data which, contrary to most others available in the literature, is not based on the proportional hazards assumption.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
12/01/2018

The semi-Markov beta-Stacy process: a Bayesian non-parametric prior for semi-Markov processes

The literature on Bayesian methods for the analysis of discrete-time sem...
research
02/26/2018

The Beta-Bernoulli process and algebraic effects

In this paper we analyze the Beta-Bernoulli process from Bayesian nonpar...
research
05/03/2021

A nonparametric instrumental approach to endogeneity in competing risks models

This paper discusses endogenous treatment models with duration outcomes,...
research
11/30/2020

Hawkes processes as competing hazards models and a simulation algorithm

In this paper, we establish a competing hazards interpretation of Hawkes...
research
10/19/2018

Nonparametric Bayesian Lomax delegate racing for survival analysis with competing risks

We propose Lomax delegate racing (LDR) to explicitly model the mechanism...
research
06/20/2021

Combined tests based on restricted mean time lost for competing risks data

Competing risks data are common in medical studies, and the sub-distribu...
research
11/07/2019

Scalable Algorithms for Large Competing Risks Data

This paper develops two orthogonal contributions to scalable sparse regr...

Please sign up or login with your details

Forgot password? Click here to reset