Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/93906
Title: | Semiparametric Bayesian analysis for longitudinal mixed effects models with non-normal AR(1) errors | Authors: | Shen, J Yu, H Yang, J Liu, C |
Issue Date: | May-2019 | Source: | Statistics and computing, May 2019, v. 29, no. 3, p. 571-583 | Abstract: | This paper studies Bayesian inference on longitudinal mixed effects models with non-normal AR(1) errors. We model the nonparametric zero-mean noise in the autoregression residual with a Dirichlet process (DP) mixture model. Applying the empirical likelihood tool, an adjusted sampler based on the Pólya urn representation of DP is proposed to incorporate information of the moment constraints of the mixing distribution. A Gibbs sampling algorithm based on the adjusted sampler is proposed to approximate the posterior distributions under DP priors. The proposed method can easily be extended to address other moment constraints owing to the wide application background of the empirical likelihood. Simulation studies are used to evaluate the performance of the proposed method. Our method is illustrated via the analysis of a longitudinal dataset from a psychiatric study. | Keywords: | Autocorrelation Dirichlet process mixture models Empirical likelihood Pólya urn representation Random effects |
Publisher: | Springer | Journal: | Statistics and computing | ISSN: | 0960-3174 | EISSN: | 1573-1375 | DOI: | 10.1007/s11222-018-9824-4 | Rights: | © Springer Science+Business Media, LLC, part of Springer Nature 2018 This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use (https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms), but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s11222-018-9824-4 |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Yang_Semiparametric_Bayesian_Analysis.pdf | Pre-Published version | 793.94 kB | Adobe PDF | View/Open |
Page views
48
Last Week
1
1
Last month
Citations as of Apr 28, 2024
Downloads
41
Citations as of Apr 28, 2024
SCOPUSTM
Citations
1
Citations as of May 3, 2024
WEB OF SCIENCETM
Citations
1
Citations as of May 2, 2024
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.