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Gibbs sampling in DP-based nonlinear mixed effects models

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  • Jing Wang

Abstract

This article uses several approaches to deal with the difficulty involved in evaluating the intractable integral when using Gibbs sampling to estimate the nonlinear mixed effects model (NLMM) based on the Dirichlet process (DP). For illustration, we applied these approaches to real data and simulations. Comparisons are then made between these methods with respect to estimation accuracy and computing efficiency.

Suggested Citation

  • Jing Wang, 2010. "Gibbs sampling in DP-based nonlinear mixed effects models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(2), pages 325-340.
    • Handle: RePEc:taf:japsta:v:37:y:2010:i:2:p:325-340
    DOI: 10.1080/02664760903117721
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    References listed on IDEAS

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    1. Peter J. Green & Sylvia Richardson, 2001. "Modelling Heterogeneity With and Without the Dirichlet Process," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 28(2), pages 355-375, June.
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