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Efficient Bayesian nonparametric hazard regression

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  • Kaeding, Matthias

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.

Suggested Citation

  • Kaeding, Matthias, 2020. "Efficient Bayesian nonparametric hazard regression," Ruhr Economic Papers 850, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    • Handle: RePEc:zbw:rwirep:850
    DOI: 10.4419/86788985
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    References listed on IDEAS

    as
    1. Thomas Kneib & Ludwig Fahrmeir, 2007. "A Mixed Model Approach for Geoadditive Hazard Regression," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 34(1), pages 207-228, March.
    2. Eilers, Lea, 2017. "Is my rental price overestimated? A small area index for Germany," Ruhr Economic Papers 734, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    3. Cai, Bo & Lin, Xiaoyan & Wang, Lianming, 2011. "Bayesian proportional hazards model for current status data with monotone splines," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2644-2651, September.
    4. Luis E. Nieto‐Barajas & Stephen G. Walker, 2002. "Markov Beta and Gamma Processes for Modelling Hazard Rates," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 29(3), pages 413-424, September.
    5. Boelmann, Barbara & Schaffner, Sandra, 2018. "FDZ data description: Real-estate data for Germany (RWI-GEO-RED). Advertisements on the internet platform ImmobilienScout24," RWI Projektberichte, RWI - Leibniz-Institut für Wirtschaftsforschung, number 195940, December.
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    7. Brezger, Andreas & Steiner, Winfried J., 2008. "Monotonic Regression Based on Bayesian PSplines: An Application to Estimating Price Response Functions From Store-Level Scanner Data," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 90-104, January.
    8. Haiming Zhou & Timothy Hanson, 2018. "A Unified Framework for Fitting Bayesian Semiparametric Models to Arbitrarily Censored Survival Data, Including Spatially Referenced Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(522), pages 571-581, April.
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    More about this item

    Keywords

    Bayesian survival analysis; nonparametric modeling; penalized spline: restricted mean survival time;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C41 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Duration Analysis; Optimal Timing Strategies

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