Comparing distributions by using dependent normalized random-measure mixtures
A methodology for the simultaneous Bayesian non-parametric modelling of several distributions is developed. Our approach uses normalized random measures with independent increments and builds dependence through the superposition of shared processes. The properties of the prior are described and the...
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| Veröffentlicht in: | Journal of the Royal Statistical Society. Series B, Statistical methodology Statistical methodology, 2013-06, Vol.75 (3), p.499-529 |
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| container_end_page | 529 |
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| container_issue | 3 |
| container_start_page | 499 |
| container_title | Journal of the Royal Statistical Society. Series B, Statistical methodology |
| container_volume | 75 |
| creator | Griffin, J. E. Kolossiatis, M. Steel, M. F. J. |
| description | A methodology for the simultaneous Bayesian non-parametric modelling of several distributions is developed. Our approach uses normalized random measures with independent increments and builds dependence through the superposition of shared processes. The properties of the prior are described and the modelling possibilities of this framework are explored in detail. Efficient slice sampling methods are developed for inference. Various posterior summaries are introduced which allow better understanding of the differences between distributions. The methods are illustrated on simulated data and examples from survival analysis and stochastic frontier analysis. |
| doi_str_mv | 10.1111/rssb.12002 |
| format | Article |
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E.</creatorcontrib><creatorcontrib>Kolossiatis, M.</creatorcontrib><creatorcontrib>Steel, M. F. J.</creatorcontrib><title>Comparing distributions by using dependent normalized random-measure mixtures</title><title>Journal of the Royal Statistical Society. Series B, Statistical methodology</title><addtitle>J. R. Stat. Soc. B</addtitle><description>A methodology for the simultaneous Bayesian non-parametric modelling of several distributions is developed. Our approach uses normalized random measures with independent increments and builds dependence through the superposition of shared processes. The properties of the prior are described and the modelling possibilities of this framework are explored in detail. Efficient slice sampling methods are developed for inference. Various posterior summaries are introduced which allow better understanding of the differences between distributions. The methods are illustrated on simulated data and examples from survival analysis and stochastic frontier analysis.</description><subject>Average linear density</subject><subject>Bayesian analysis</subject><subject>Bayesian method</subject><subject>Bayesian non-parametrics</subject><subject>Computer simulation</subject><subject>Construction</subject><subject>Density</subject><subject>Dependent distributions</subject><subject>Dirichlet process</subject><subject>Inference</subject><subject>Mathematical models</subject><subject>Methodology</subject><subject>Modelling</subject><subject>Multilevel models</subject><subject>Nonprofit hospitals</subject><subject>Normalized generalized gamma process</subject><subject>Point masses</subject><subject>Probability distributions</subject><subject>Random sampling</subject><subject>Reliability functions</subject><subject>Sampling</subject><subject>Simulation</subject><subject>Slice sampling</subject><subject>Statistics</subject><subject>Steels</subject><subject>Stochastic processes</subject><subject>Studies</subject><subject>Survival</subject><subject>Survival analysis</subject><subject>Utility function</subject><issn>1369-7412</issn><issn>1467-9868</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>24P</sourceid><recordid>eNqFkc-LEzEUxwdRsNa9eBcGvMjC1PzOzFGLVqWrsFvpMSSTN5I6M6nJDNvuX7_pjvbgwX0EXsj38w28982yVxgtcKp3IUazwAQh8iSbYSZkUZWifJruVFSFZJg8z17EuEOphKSz7Grpu70Orv-ZWxeH4Mw4ON_H3BzzMT48wx56C_2Q9z50unV3YPOge-u7ogMdxwB55w5D6vFl9qzRbYSLP32e_fj0cbP8XKy_r74s36-LmglKCqEZM8ZwCaimwAUyFlWmYaVGJh3LeIk5ZXXDRFNRbDHYptEVECRKC5bQefZ2-ncf_O8R4qA6F2toW92DH6PCDJdpC5jQx1EqiSBCMpbQN_-gOz-GPg2SKM4ZphWqEnU5UXXwMQZo1D64ToejwkidQlCnENRDCAnGE3zrWjj-h1TXNzcf_npeT55dHHw4ewiTkkjKk15MesoLDmddh18qRSq52n5bqe0Kl7LcbNVXeg9TbaL1</recordid><startdate>201306</startdate><enddate>201306</enddate><creator>Griffin, J. 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| identifier | ISSN: 1369-7412 |
| ispartof | Journal of the Royal Statistical Society. Series B, Statistical methodology, 2013-06, Vol.75 (3), p.499-529 |
| issn | 1369-7412 1467-9868 |
| language | eng |
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| source | Jstor Complete Legacy; Oxford University Press Journals All Titles (1996-Current); Wiley Online Library Journals Frontfile Complete; Business Source Complete; JSTOR Mathematics & Statistics |
| subjects | Average linear density Bayesian analysis Bayesian method Bayesian non-parametrics Computer simulation Construction Density Dependent distributions Dirichlet process Inference Mathematical models Methodology Modelling Multilevel models Nonprofit hospitals Normalized generalized gamma process Point masses Probability distributions Random sampling Reliability functions Sampling Simulation Slice sampling Statistics Steels Stochastic processes Studies Survival Survival analysis Utility function |
| title | Comparing distributions by using dependent normalized random-measure mixtures |
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