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.
BibTeX
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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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)