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Bayesian Functional Data Analysis Using WinBUGS

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  • Crainiceanu, Ciprian M.
  • Goldsmith, A. Jeffrey

Abstract

We provide user friendly software for Bayesian analysis of functional data models using pkg{WinBUGS}~1.4. The excellent properties of Bayesian analysis in this context are due to: (1) dimensionality reduction, which leads to low dimensional projection bases; (2) mixed model representation of functional models, which provides a modular approach to model extension; and (3) orthogonality of the principal component bases, which contributes to excellent chain convergence and mixing properties. Our paper provides one more, essential, reason for using Bayesian analysis for functional models: the existence of software.

Suggested Citation

  • Crainiceanu, Ciprian M. & Goldsmith, A. Jeffrey, 2010. "Bayesian Functional Data Analysis Using WinBUGS," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 32(i11).
    • Handle: RePEc:jss:jstsof:v:032:i11
    DOI: http://hdl.handle.net/10.18637/jss.v032.i11
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    Cited by:

    1. Agnese Maria Di Brisco & Enea Giuseppe Bongiorno & Aldo Goia & Sonia Migliorati, 2023. "Bayesian flexible beta regression model with functional covariate," Computational Statistics, Springer, vol. 38(2), pages 623-645, June.
    2. Febrero-Bande, Manuel & de la Fuente, Manuel Oviedo, 2012. "Statistical Computing in Functional Data Analysis: The R Package fda.usc," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 51(i04).
    3. Guodong Shan & Yiheng Hou & Baisen Liu, 2020. "Bayesian robust estimation of partially functional linear regression models using heavy-tailed distributions," Computational Statistics, Springer, vol. 35(4), pages 2077-2092, December.
    4. Shang, Han Lin & Smith, Peter W.F. & Bijak, Jakub & Wiśniowski, Arkadiusz, 2016. "A multilevel functional data method for forecasting population, with an application to the United Kingdom," International Journal of Forecasting, Elsevier, vol. 32(3), pages 629-649.
    5. Eve Bohnett & Jessica Schulz & Robert Dobbs & Thomas Hoctor & Dave Hulse & Bilal Ahmad & Wajid Rashid & Hardin Waddle, 2023. "Shorebird Monitoring Using Spatially Explicit Occupancy and Abundance," Land, MDPI, vol. 12(4), pages 1-15, April.
    6. Codazzi, Laura & Colombi, Alessandro & Gianella, Matteo & Argiento, Raffaele & Paci, Lucia & Pini, Alessia, 2022. "Gaussian graphical modeling for spectrometric data analysis," Computational Statistics & Data Analysis, Elsevier, vol. 174(C).
    7. Li, Kan & Luo, Sheng, 2019. "Bayesian functional joint models for multivariate longitudinal and time-to-event data," Computational Statistics & Data Analysis, Elsevier, vol. 129(C), pages 14-29.
    8. Kathrine Frey Frøslie & Jo Røislien & Elisabeth Qvigstad & Kristin Godang & Jens Bollerslev & Tore Henriksen & Marit B Veierød, 2014. "Shape Information in Repeated Glucose Curves during Pregnancy Provided Significant Physiological Information for Neonatal Outcomes," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-10, March.

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