Efficient Bayesian nonparametric hazard regression

Matthias Kaeding

No 850, Ruhr Economic Papers from RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen

Abstract: We model the log-cumulative baseline hazard for the Cox model via Bayesian, monotonic P-splines. This approach permits fast computation, accounting for arbitrary censorship and the inclusion of nonparametric effects. We leverage the computational efficiency to simplify effect interpretation for metric and non-metric variables by combining the restricted mean survival time approach with partial dependence plots. This allows effect interpretation in terms of survival times. Monte Carlo simulations indicate that the proposed methods work well. We illustrate our approach using a large data set of real estate data advertisements.

Keywords: Bayesian survival analysis; nonparametric modeling; penalized spline: restricted mean survival time (search for similar items in EconPapers)
JEL-codes: C11 C14 C41 (search for similar items in EconPapers)
Date: 2020
New Economics Papers: this item is included in nep-ecm
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:rwirep:850

DOI: 10.4419/86788985

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