Quotation Knaus, Peter, Winkler, Daniel. 2021. A Bayesian Survival Model for Time-Varying Coefficients and unobserved Heterogeneity. European Young Statisticians Meeting, Athens, Greece, 06.09-10.09.


RIS


BibTeX

Abstract

Two sources of heterogeneity are often overlooked in the applied survival literature. On the one hand, time-varying hazard contributions of explanatory variables cannot be captured in the widely used Cox proportional hazard model. To this end, this paper investigates a dynamic survival model in the spirit of [3] within a Bayesian framework. Such a specification allows parameters to gradually evolve over time, thus accounting for time-varying effects. On the other hand, unobserved heterogeneity across (a potentially large number of) groups is often ignored, leading to invalid estimators. This paper makes accounting for such effects feasible for even large numbers of groups through a shared factor model, which picks up unexplained covariance in the error term. Building on the Markov Chain Monte Carlo scheme of [4] allows the usage of shrinkage priors to avoid overfitting in such a highly parameterized model. This paper uses the riple gamma prior introduced by [2] in the same fashion as [1] to detectwhich parameters should be included in the model and which should be allowed to vary over time. Finally, an R package which makes the routine easily available is introduced.

Tags

Press 'enter' for creating the tag

Publication's profile

Status of publication Published
Affiliation WU
Type of publication Paper presented at an academic conference or symposium
Language English
Title A Bayesian Survival Model for Time-Varying Coefficients and unobserved Heterogeneity
Event European Young Statisticians Meeting
Year 2021
Date 06.09-10.09
Country Greece
Location Athens

Associations

People
Knaus, Peter (Details)
Winkler, Daniel (Details)
Organization
Institute for Statistics and Mathematics IN (Details)
Research areas (Ă–STAT Classification 'Statistik Austria')
1105 Computer software (Details)
5323 Econometrics (Details)
5701 Applied statistics (Details)
Google Scholar: Search