Bayesian Analysis of Hazard Regression Models under Order Restrictions on Covariate Effects and Ageing

Arnab Bhattacharjee, Madhuchhanda Bhattacharjee

    Research output: Working paper/PreprintWorking paper

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    We propose Bayesian inference in hazard regression models where the baseline hazard is unknown, covariate effects are possibly age-varying (non-proportional), and there is multiplicative frailty with arbitrary distribution. Our framework incorporates a wide variety of order restrictions on covariate dependence and duration dependence (ageing). We propose estimation and evaluation of age-varying covariate effects when covariate dependence is monotone rather than proportional. In particular, we consider situations where the lifetime conditional on a higher value of the covariate ages faster or slower than that conditional on a lower value; this kind of situation is common in applications. In addition, there may be restrictions on the nature of ageing. For example, relevant theory may suggest that the baseline hazard function decreases with age. The proposed framework enables evaluation of order restrictions in the nature of both covariate and duration dependence as well as estimation of hazard regression models under such restrictions. The usefulness of the proposed Bayesian model and inference methods are illustrated with an application to corporate bankruptcies in the UK.
    Original languageEnglish
    Place of PublicationMunich
    PublisherUniversity Library of Munich, Germany
    Number of pages27
    Publication statusPublished - 2007

    Publication series

    Publisheruniversity of Munich


    • bayesian nonparametrics
    • Non-proportional hazards
    • Frailty
    • Age-varying covariate effects
    • Ageing


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