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Nonparametric Bayes inference for concave distribution functions

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  • Martin B. Hansen
  • Steffen L. Lauritzen

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.

Suggested Citation

  • Martin B. Hansen & Steffen L. Lauritzen, 2002. "Nonparametric Bayes inference for concave distribution functions," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 56(1), pages 110-127, February.
    • Handle: RePEc:bla:stanee:v:56:y:2002:i:1:p:110-127
    DOI: 10.1111/1467-9574.04600
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    Cited by:

    1. Tomasz Rychlik, 2019. "Sharp bounds on distribution functions and expectations of mixtures of ordered families of distributions," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(1), pages 166-195, March.
    2. Nora Saadi & Smail Adjabi & Ali Gannoun, 2018. "The selection of the number of terms in an orthogonal series cumulative function estimator," Statistical Papers, Springer, vol. 59(1), pages 127-152, March.
    3. Athanasios Kottas & Milovan Krnjajić, 2009. "Bayesian Semiparametric Modelling in Quantile Regression," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(2), pages 297-319, June.

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