Document Detail

Bayesian estimation of semiparametric nonlinear dynamic factor analysis models using the Dirichlet process prior.
MedLine Citation:
PMID:  21506946     Owner:  NLM     Status:  MEDLINE    
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.
Sy-Miin Chow; Niansheng Tang; Ying Yuan; Xinyuan Song; Hongtu Zhu
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Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't; Research Support, U.S. Gov't, Non-P.H.S.    
Journal Detail:
Title:  The British journal of mathematical and statistical psychology     Volume:  64     ISSN:  0007-1102     ISO Abbreviation:  Br J Math Stat Psychol     Publication Date:  2011 Feb 
Date Detail:
Created Date:  2011-04-21     Completed Date:  2011-06-21     Revised Date:  2014-09-21    
Medline Journal Info:
Nlm Unique ID:  0004047     Medline TA:  Br J Math Stat Psychol     Country:  England    
Other Details:
Languages:  eng     Pagination:  69-106     Citation Subset:  IM    
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MeSH Terms
Bayes Theorem*
Factor Analysis, Statistical*
Infant, Newborn
Nonlinear Dynamics*
Psychology / statistics & numerical data*
Psychometrics / statistics & numerical data
Statistics, Nonparametric
Stochastic Processes*
Young Adult
Grant Support
AG033387/AG/NIA NIH HHS; MH086633/MH/NIMH NIH HHS; P01 CA142538/CA/NCI NIH HHS; P01CA142538-01/CA/NCI NIH HHS; R01 MH086633/MH/NIMH NIH HHS; R21 AG033387/AG/NIA NIH HHS; UL1-RR025747-01/RR/NCRR NIH HHS

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