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

Br J Math Stat Psychol. 2011 Feb;64(Pt 1):69-106. doi: 10.1348/000711010X497262.


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.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Affect
  • Bayes Theorem*
  • Factor Analysis, Statistical*
  • Female
  • Humans
  • Individuality
  • Infant, Newborn
  • Male
  • Nonlinear Dynamics*
  • Probability
  • Psychology / statistics & numerical data*
  • Psychometrics / statistics & numerical data
  • Statistics, Nonparametric
  • Stochastic Processes*
  • Young Adult