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A Bayesian nonparametric study of a dynamic nonlinear model

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  • Hatjispyros, Spyridon J.
  • Nicoleris, Theodoros
  • Walker, Stephen G.

Abstract

A Bayesian nonparametric approach to modeling a nonlinear dynamic model is presented. New techniques for sampling infinite mixture models are used. The inference procedure specifically in the case of the logistic model and when the nonparametric component is applied to the additive errors is demonstrated.

Suggested Citation

  • Hatjispyros, Spyridon J. & Nicoleris, Theodoros & Walker, Stephen G., 2009. "A Bayesian nonparametric study of a dynamic nonlinear model," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 3948-3956, October.
    • Handle: RePEc:eee:csdana:v:53:y:2009:i:12:p:3948-3956
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    References listed on IDEAS

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    1. P. Damlen & J. Wakefield & S. Walker, 1999. "Gibbs sampling for Bayesian non‐conjugate and hierarchical models by using auxiliary variables," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(2), pages 331-344, April.
    2. Tang, Yongqiang & Ghosal, Subhashis, 2007. "A consistent nonparametric Bayesian procedure for estimating autoregressive conditional densities," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4424-4437, May.
    3. Hatjispyros, S.J. & Yannacopoulos, A.N., 2005. "A random dynamical system model of a stylized equity market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 347(C), pages 583-612.
    4. Hatjispyros, S.J. & Nicoleris, Theodoros & Walker, Stephen G., 2007. "Parameter estimation for random dynamical systems using slice sampling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 381(C), pages 71-81.
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    Cited by:

    1. Lee, Kyoungjae & Lee, Jaeyong & Dass, Sarat C., 2018. "Inference for differential equation models using relaxation via dynamical systems," Computational Statistics & Data Analysis, Elsevier, vol. 127(C), pages 116-134.
    2. Hatjispyros, Spyridon J. & Nicoleris, Theodoros & Walker, Stephen G., 2011. "Dependent mixtures of Dirichlet processes," Computational Statistics & Data Analysis, Elsevier, vol. 55(6), pages 2011-2025, June.
    3. Christos Merkatas & Simo Särkkä, 2023. "System identification using autoregressive Bayesian neural networks with nonparametric noise models," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(3), pages 319-330, May.

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