Bayesian estimation of semiparametric nonlinear dynamic factor analysis models using the Dirichlet process prior

Sy Miin Chow, Niansheng Tang, Ying Yuan, Xinyuan Song, Hongtu Zhu

Research output: Contribution to journalArticle

30 Scopus citations


Parameters in time series and other dynamic models often show complex range restrictions and their distributions may deviate substantially from multivariate normal or other standard parametric distributions. We use the truncated Dirichlet process (DP) as a non-parametric prior for such dynamic parameters in a novel nonlinear Bayesian dynamic factor analysis model. This is equivalent to specifying the prior distribution to be a mixture distribution composed of an unknown number of discrete point masses (or clusters). The stick-breaking prior and the blocked Gibbs sampler are used to enable efficient simulation of posterior samples. Using a series of empirical and simulation examples, we illustrate the flexibility of the proposed approach in approximating distributions of very diverse shapes.

Original languageEnglish (US)
Pages (from-to)69-106
Number of pages38
JournalBritish Journal of Mathematical and Statistical Psychology
Issue number1
StatePublished - Feb 2011


All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Arts and Humanities (miscellaneous)
  • Psychology(all)

Cite this