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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

Authors


Hansen, MB More by this author
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Institution:
University of Oxford
Department:
Oxford, MPLS, Statistics
Journal:
STATISTICA NEERLANDICA
Volume:
56
Issue:
1
Pages:
110-127
Publication date:
2002-02-05
DOI:
EISSN:
1467-9574
ISSN:
0039-0402
URN:
uuid:2debf95b-3843-4264-b504-b9bfde1adce6
Source identifiers:
97562
Local pid:
pubs:97562

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