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Allowing for the effect of data binning in a Bayesian Normal mixture model

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  • Alston, C.L.
  • Mengersen, K.L.

Abstract

The usual Gibbs sampling framework of the Bayesian mixture model is extended to account for binned data. This model involves the addition of a latent variable in the model which represents simulated values from the believed true distribution at each iteration of the algorithm. The technique results in better model fit and recognition of the more subtle aspects of the density of the data.

Suggested Citation

  • Alston, C.L. & Mengersen, K.L., 2010. "Allowing for the effect of data binning in a Bayesian Normal mixture model," Computational Statistics & Data Analysis, Elsevier, vol. 54(4), pages 916-923, April.
    • Handle: RePEc:eee:csdana:v:54:y:2010:i:4:p:916-923
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    References listed on IDEAS

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    1. P. Wild & W. R. Gilks, 1993. "Adaptive Rejection Sampling from Log‐Concave Density Functions," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 42(4), pages 701-709, December.
    2. Sylvia. Richardson & Peter J. Green, 1997. "On Bayesian Analysis of Mixtures with an Unknown Number of Components (with discussion)," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(4), pages 731-792.
    3. repec:dau:papers:123456789/1906 is not listed on IDEAS
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    Cited by:

    1. Murasawa, Yasutomo, 2017. "Measuring the Distributions of Public Inflation Perceptions and Expectations in the UK," MPRA Paper 76244, University Library of Munich, Germany.
    2. Yasutomo Murasawa, 2020. "Measuring public inflation perceptions and expectations in the UK," Empirical Economics, Springer, vol. 59(1), pages 315-344, July.

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