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

TitleBayesian estimation of semiparametric nonlinear dynamic factor analysis models using the Dirichlet process prior.
Publication TypeJournal Article
Year of Publication2011
AuthorsChow, Sy-Miin, Niansheng Tang, Ying Yuan, Xinyuan Song, and Hongtu Zhu
JournalBr J Math Stat Psychol
Volume64
IssuePt 1
Pagination69-106
Date Published2011 Feb
ISSN0007-1102
KeywordsAffect, Bayes Theorem, Factor Analysis, Statistical, Female, Humans, Individuality, Infant, Newborn, Male, Nonlinear Dynamics, Probability, Psychology, Psychometrics, Statistics, Nonparametric, Stochastic Processes, Young Adult
Abstract

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.

DOI10.1348/000711010X497262
Alternate JournalBr J Math Stat Psychol
Original PublicationBayesian estimation of semiparametric nonlinear dynamic factor analysis models using the Dirichlet process prior.
PubMed ID21506946
PubMed Central IDPMC3199348
Grant ListAG033387 / AG / NIA NIH HHS / United States
P01CA142538-01 / CA / NCI NIH HHS / United States
R01 MH086633 / MH / NIMH NIH HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
MH086633 / MH / NIMH NIH HHS / United States
UL1 RR025747 / RR / NCRR NIH HHS / United States
UL1-RR025747-01 / RR / NCRR NIH HHS / United States
R21 AG033387 / AG / NIA NIH HHS / United States
Project: