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Nonparametric Bayes inference for concave distribution functions
Author(s) -
Hansen Martin B.,
Lauritzen Steffen L.
Publication year - 2002
Publication title -
statistica neerlandica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.52
H-Index - 39
eISSN - 1467-9574
pISSN - 0039-0402
DOI - 10.1111/1467-9574.04600
Subject(s) - mathematics , markov chain monte carlo , dirichlet process , dirichlet distribution , bayesian inference , posterior probability , statistics , bayesian probability , mathematical analysis , boundary value problem
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.