Quotation Knaus, Peter, Winkler, Daniel. 2021. A Bayesian Survival Model for Time-Varying Coefficients and unobserved Heterogeneity. 4th International Conference on Econometrics and Statistics, Hong Kong, China, 24.06-26.06.


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Abstract

Two sources of heterogeneity are often overlooked. 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, a dynamic survival model is investigated within a Bayesian framework. The specification allows parameters to evolve over time, thus accounting for time-varying effects gradually. On the other hand, unobserved heterogeneity across groups is often ignored, leading to invalid estimators. Accounting for such effects is made feasible for even large numbers of groups through a shared factor model, which picks up unexplained covariance in the error term. Building on a Markov Chain Monte Carlo scheme based on data augmentation allows the usage of shrinkage priors to avoid overfitting in such a highly parameterized model. In particular, the shrinkage priors are implemented to automatically detect which parameters should be included in the model and which should be allowed to vary over time. Finally, an R package that makes the routine easily available is introduced.

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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 4th International Conference on Econometrics and Statistics
Year 2021
Date 24.06-26.06
Country China
Location Hong Kong

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)
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