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Nonparametric Bayesian estimation of a bivariate density with interval censored data

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  • Yang, Mingan
  • Hanson, Timothy
  • Christensen, Ronald

Abstract

Mixture of Polya trees nonparametric estimation of a bivariate density is presented for interval censored data. Real and simulated data are analyzed and compared with nonparametric maximum likelihood (NPMLE) and Bayesian G-spline estimates. An advantage of the mixture of Polya trees approach over the NPMLE is the relative ease with which continuous bivariate density and hazard plots are obtained.

Suggested Citation

  • Yang, Mingan & Hanson, Timothy & Christensen, Ronald, 2008. "Nonparametric Bayesian estimation of a bivariate density with interval censored data," Computational Statistics & Data Analysis, Elsevier, vol. 52(12), pages 5202-5214, August.
  • Handle: RePEc:eee:csdana:v:52:y:2008:i:12:p:5202-5214
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    References listed on IDEAS

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    1. Berger J. O & Guglielmi A., 2001. "Bayesian and Conditional Frequentist Testing of a Parametric Model Versus Nonparametric Alternatives," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 174-184, March.
    2. Hanson, Timothy E., 2006. "Inference for Mixtures of Finite Polya Tree Models," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1548-1565, December.
    3. Lo, Shaw-Hwa & Wang, Jane-Ling, 1989. "I.i.d. representations for the bivariate product limit estimators and the bootstrap versions," Journal of Multivariate Analysis, Elsevier, vol. 28(2), pages 211-226, February.
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    Cited by:

    1. Argiento, Raffaele & Guglielmi, Alessandra & Pievatolo, Antonio, 2010. "Bayesian density estimation and model selection using nonparametric hierarchical mixtures," Computational Statistics & Data Analysis, Elsevier, vol. 54(4), pages 816-832, April.
    2. Lambert, Philippe, 2011. "Smooth semiparametric and nonparametric Bayesian estimation of bivariate densities from bivariate histogram data," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 429-445, January.

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