IDEAS home Printed from https://ideas.repec.org/a/spr/psycho/v77y2012i4p670-692.html
   My bibliography  Save this article

Modeling Associations Among Multivariate Longitudinal Categorical Variables in Survey Data: A Semiparametric Bayesian Approach

Author

Listed:
  • Sylvie Tchumtchoua
  • Dipak Dey

Abstract

This paper proposes a semiparametric Bayesian framework for the analysis of associations among multivariate longitudinal categorical variables in high-dimensional data settings. This type of data is frequent, especially in the social and behavioral sciences. A semiparametric hierarchical factor analysis model is developed in which the distributions of the factors are modeled nonparametrically through a dynamic hierarchical Dirichlet process prior. A Markov chain Monte Carlo algorithm is developed for fitting the model, and the methodology is exemplified through a study of the dynamics of public attitudes toward science and technology in the United States over the period 1992–2001. Copyright The Psychometric Society 2012

Suggested Citation

  • Sylvie Tchumtchoua & Dipak Dey, 2012. "Modeling Associations Among Multivariate Longitudinal Categorical Variables in Survey Data: A Semiparametric Bayesian Approach," Psychometrika, Springer;The Psychometric Society, vol. 77(4), pages 670-692, October.
    • Handle: RePEc:spr:psycho:v:77:y:2012:i:4:p:670-692
    DOI: 10.1007/s11336-012-9274-4
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s11336-012-9274-4
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11336-012-9274-4?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. McLachlan, G. J. & Peel, D. & Bean, R. W., 2003. "Modelling high-dimensional data by mixtures of factor analyzers," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 379-388, January.
    2. Emanuel Moench & Serena Ng, 2011. "A hierarchical factor analysis of U.S. housing market dynamics," Econometrics Journal, Royal Economic Society, vol. 14(1), pages 1-24, February.
    3. Hee‐Je Bak, 2001. "Education and Public Attitudes toward Science: Implications for the “Deficit Model” of Education and Support for Science and Technology," Social Science Quarterly, Southwestern Social Science Association, vol. 82(4), pages 779-795, December.
    4. A. Bhattacharya & D. B. Dunson, 2011. "Sparse Bayesian infinite factor models," Biometrika, Biometrika Trust, vol. 98(2), pages 291-306.
    5. Teh, Yee Whye & Jordan, Michael I. & Beal, Matthew J. & Blei, David M., 2006. "Hierarchical Dirichlet Processes," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1566-1581, December.
    6. Hsinchun Chen, 2003. "Introduction to the JASIST Special Topic issue on web retrieval and mining: A machine learning perspective," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 54(7), pages 621-624, May.
    7. Mingan Yang & David Dunson, 2010. "Bayesian Semiparametric Structural Equation Models with Latent Variables," Psychometrika, Springer;The Psychometric Society, vol. 75(4), pages 675-693, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mohsen Maleki & Darren Wraith, 2019. "Mixtures of multivariate restricted skew-normal factor analyzer models in a Bayesian framework," Computational Statistics, Springer, vol. 34(3), pages 1039-1053, September.
    2. Bai, Jushan & Ando, Tomohiro, 2013. "Multifactor asset pricing with a large number of observable risk factors and unobservable common and group-specific factors," MPRA Paper 52785, University Library of Munich, Germany, revised Dec 2013.
    3. Kim, Gwangsu & Choi, Taeryon, 2019. "Asymptotic properties of nonparametric estimation and quantile regression in Bayesian structural equation models," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 68-82.
    4. Michelle Dietzen & Haoran Zhai & Olivia Lucas & Oriol Pich & Christopher Barrington & Wei-Ting Lu & Sophia Ward & Yanping Guo & Robert E. Hynds & Simone Zaccaria & Charles Swanton & Nicholas McGranaha, 2024. "Replication timing alterations are associated with mutation acquisition during breast and lung cancer evolution," Nature Communications, Nature, vol. 15(1), pages 1-23, December.
    5. Redivo, Edoardo & Nguyen, Hien D. & Gupta, Mayetri, 2020. "Bayesian clustering of skewed and multimodal data using geometric skewed normal distributions," Computational Statistics & Data Analysis, Elsevier, vol. 152(C).
    6. Parvin Ahmadi & Iman Gholampour & Mahmoud Tabandeh, 2018. "Cluster-based sparse topical coding for topic mining and document clustering," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(3), pages 537-558, September.
    7. Vainio, Annukka & Paloniemi, Riikka, 2014. "The complex role of attitudes toward science in pro-environmental consumption in the Nordic countries," Ecological Economics, Elsevier, vol. 108(C), pages 18-27.
    8. Conti, Gabriella & Frühwirth-Schnatter, Sylvia & Heckman, James J. & Piatek, Rémi, 2014. "Bayesian exploratory factor analysis," Journal of Econometrics, Elsevier, vol. 183(1), pages 31-57.
    9. Niko Hauzenberger & Maximilian Bock & Michael Pfarrhofer & Anna Stelzer & Gregor Zens, 2018. "Implications of macroeconomic volatility in the Euro area," Papers 1801.02925, arXiv.org, revised Jun 2018.
    10. Papastamoulis, Panagiotis, 2018. "Overfitting Bayesian mixtures of factor analyzers with an unknown number of components," Computational Statistics & Data Analysis, Elsevier, vol. 124(C), pages 220-234.
    11. Hideaki Hirata & M. Ayhan Kose & Christopher Otrok & Marco E Terrones, 2013. "Global House Price Fluctuations: Synchronization and Determinants," NBER International Seminar on Macroeconomics, University of Chicago Press, vol. 9(1), pages 119-166.
    12. Jeffrey L. Furman & Florenta Teodoridis, 2020. "Automation, Research Technology, and Researchers’ Trajectories: Evidence from Computer Science and Electrical Engineering," Organization Science, INFORMS, vol. 31(2), pages 330-354, March.
    13. Matthew W. Wheeler, 2019. "Bayesian additive adaptive basis tensor product models for modeling high dimensional surfaces: an application to high‐throughput toxicity testing," Biometrics, The International Biometric Society, vol. 75(1), pages 193-201, March.
    14. Shu-Ping Shi & Yong Song, 2012. "Identifying Speculative Bubbles with an Infinite Hidden Markov Model," Working Paper series 26_12, Rimini Centre for Economic Analysis.
    15. Montanari, Angela & Viroli, Cinzia, 2011. "Maximum likelihood estimation of mixtures of factor analyzers," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2712-2723, September.
    16. Joshua Chan, 2023. "BVARs and Stochastic Volatility," Papers 2310.14438, arXiv.org.
    17. Cesa-Bianchi, Ambrogio, 2013. "Housing cycles and macroeconomic fluctuations: A global perspective," Journal of International Money and Finance, Elsevier, vol. 37(C), pages 215-238.
    18. Zhang, Q. & Ip, E.H., 2014. "Variable assessment in latent class models," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 146-156.
    19. Jin, Xin & Maheu, John M. & Yang, Qiao, 2022. "Infinite Markov pooling of predictive distributions," Journal of Econometrics, Elsevier, vol. 228(2), pages 302-321.
    20. Durante, Daniele, 2017. "A note on the multiplicative gamma process," Statistics & Probability Letters, Elsevier, vol. 122(C), pages 198-204.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:psycho:v:77:y:2012:i:4:p:670-692. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.