Barcella, W;
De Iorio, M;
Favaro, S;
Rosner, G;
(2017)
Dependent generalized Dirichlet process priors for the analysis of acute lymphoblastic leukemia.
Biostatistics
10.1093/biostatistics/kxx042.
(In press).
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Abstract
We propose a novel Bayesian nonparametric process prior for modeling a collection of random discrete distributions. This process is defined by including a suitable Beta regression framework within a generalized Dirichlet process to induce dependence among the discrete random distributions. This strategy allows for covariate dependent clustering of the observations. Some advantages of the proposed approach include wide applicability, ease of interpretation, and availability of efficient MCMC algorithms. The motivation for this work is the study of the impact of asparginage metabolism on lipid levels in a group of pediatric patients treated for acute lymphoblastic leukemia.
| Type: | Article |
|---|---|
| Title: | Dependent generalized Dirichlet process priors for the analysis of acute lymphoblastic leukemia |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1093/biostatistics/kxx042 |
| Publisher version: | https://doi.org/10.1093/biostatistics/kxx042 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Bayesian nonparametrics; Beta regression; Dependent random probability measures; Generalized Dirichlet process; Stick-breaking processes. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Statistical Science |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10026061 |
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