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Nonparametric Bayes inference for concave distribution functions

Abstract:
Bayesian inference for concave distribution functions is investigated. This is made by transforming a mixture of Dirichlet processes on the space of distribution functions to the space of concave distribution functions. We give a method for sampling from the posterior distribution using a Pólya urn scheme in combination with a Markov chain Monte Carlo algorithm. The methods are extended to estimation of concave distribution functions for incompletely observed data.
Publication status:
Published

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Publisher copy:
10.1111/1467-9574.04600

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Institution:
University of Oxford
Division:
MPLS
Department:
Statistics
Role:
Author
Journal:
STATISTICA NEERLANDICA
Volume:
56
Issue:
1
Pages:
110-127
Publication date:
2002-02-01
DOI:
EISSN:
1467-9574
ISSN:
0039-0402
Source identifiers:
97562
Language:
English
Keywords:
Pubs id:
pubs:97562
UUID:
uuid:2debf95b-3843-4264-b504-b9bfde1adce6
Local pid:
pubs:97562
Deposit date:
2012-12-19

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