A Bayesian semi-parametric bivariate failure time model |
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| Affiliation: | 1. Departmento de Estadistica, ITAM, Rio Hondo 1, San Angel, 01000 Mexico, DF, Mexico;2. Institute of Mathematics, Statistics and Actuarial Science, University of Kent, Canterbury CT2 7NZ, UK;1. Department of Neurology, Massachusetts General Hospital & Harvard Medical School, 50 Blossom St. Boston, MA 02114, USA;2. Sagol School of Neuroscience, School of Psychological Sciences, Tel Aviv University, Tel Aviv 69978, Israel;3. School of Electrical Engineering, Tel Aviv University, Tel Aviv 69978, Israel;1. School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China;2. Department of Mechanical and Industrial Engineering, University of Toronto, 5 King’s College Road, Toronto, ON M5S 3G8, Canada;3. School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China;1. National Computer Network Emergency Response Technical Team/Coordination Center of China, Beijing 100029, PR China;2. Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing 100876, PR China;3. School of Science, Nanchang University, Nanchang, Jiangxi 330031, PR China;1. Optical Approaches to Brain Function Laboratory, Department of Neuroscience and Brain Technologies, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy;2. Neural Coding Laboratory, Istituto Italiano di Tecnologia, Via Morego 30, 16163 Genova, Italy;3. Neural Computation Laboratory, Center for Neuroscience and Cognitive Systems at UniTn, Istituto Italiano di Tecnologia, Corso Bettini 31, 38068 Rovereto, Italy |
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| Abstract: | In this paper we introduce a Bayesian semiparametric model for bivariate and multivariate survival data. The marginal densities are well-known nonparametric survival models and the joint density is constructed via a mixture. Our construction also defines a copula and the properties of this new copula are studied. We also consider the model in the presence of covariates and, in particular, we find a simple generalisation of the widely used frailty model, which is based on a new bivariate gamma distribution. |
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