Bayesian Analysis of Hazard Regression Models under Order Restrictions on Covariate Effects and Ageing
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.
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- Bhattacharjee, Arnab, 2004.
"A Simple Test for the Absence of Covariate Dependence in Hazard Regression Models,"
3937, University Library of Munich, Germany.
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"Estimation in hazard regression models under ordered departures from proportionality,"
Computational Statistics & Data Analysis,
Elsevier, vol. 47(3), pages 517-536, October.
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"Macro Economic Instability and Business Exit: Determinants of Failures and Acquisitions of Large UK Firms,"
Royal Economic Society Annual Conference 2002
27, Royal Economic Society.
- A. Bhattacharjee & Higson, C. & Holly, S. & Kattuman, P., 2002. "Macro Economic Instability and Business Exit: Determinants of Failures and Acquisitions of Large UK Firms," Cambridge Working Papers in Economics 0206, Faculty of Economics, University of Cambridge.
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