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Bayesian Analysis of Mass Spectrometry Proteomic Data Using Wavelet-Based Functional Mixed Models

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  • Jeffrey S. Morris
  • Philip J. Brown
  • Richard C. Herrick
  • Keith A. Baggerly
  • Kevin R. Coombes

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  • Jeffrey S. Morris & Philip J. Brown & Richard C. Herrick & Keith A. Baggerly & Kevin R. Coombes, 2008. "Bayesian Analysis of Mass Spectrometry Proteomic Data Using Wavelet-Based Functional Mixed Models," Biometrics, The International Biometric Society, vol. 64(2), pages 479-489, June.
    • Handle: RePEc:bla:biomet:v:64:y:2008:i:2:p:479-489
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    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2007.00895.x
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    References listed on IDEAS

    as
    1. Wensheng Guo, 2002. "Functional Mixed Effects Models," Biometrics, The International Biometric Society, vol. 58(1), pages 121-128, March.
    2. Jeffrey S. Morris & Raymond J. Carroll, 2006. "Wavelet‐based functional mixed models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 68(2), pages 179-199, April.
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    Citations

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    Cited by:

    1. M. Giacofci & S. Lambert-Lacroix & G. Marot & F. Picard, 2013. "Wavelet-Based Clustering for Mixed-Effects Functional Models in High Dimension," Biometrics, The International Biometric Society, vol. 69(1), pages 31-40, March.
    2. Matilde Trevisani & Arjuna Tuzzi, 2015. "A portrait of JASA: the History of Statistics through analysis of keyword counts in an early scientific journal," Quality & Quantity: International Journal of Methodology, Springer, vol. 49(3), pages 1287-1304, May.
    3. Chau, Van Vinh & von Sachs, Rainer, 2016. "Functional mixed effects wavelet estimation for spectra of replicated time series," LIDAM Discussion Papers ISBA 2016013, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    4. Bickel David R., 2013. "Simple estimators of false discovery rates given as few as one or two p-values without strong parametric assumptions," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 12(4), pages 529-543, August.
    5. Yin Song & Shufei Ge & Jiguo Cao & Liangliang Wang & Farouk S. Nathoo, 2022. "A Bayesian spatial model for imaging genetics," Biometrics, The International Biometric Society, vol. 78(2), pages 742-753, June.
    6. Michael R. Wierzbicki & Li-Bing Guo & Qing-Tao Du & Wensheng Guo, 2014. "Sparse Semiparametric Nonlinear Model With Application to Chromatographic Fingerprints," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(508), pages 1339-1349, December.
    7. Madison Giacofci & Sophie Lambert-Lacroix & Franck Picard, 2018. "Minimax wavelet estimation for multisample heteroscedastic nonparametric regression," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 30(1), pages 238-261, January.
    8. Mark J. Meyer & Brent A. Coull & Francesco Versace & Paul Cinciripini & Jeffrey S. Morris, 2015. "Bayesian function‐on‐function regression for multilevel functional data," Biometrics, The International Biometric Society, vol. 71(3), pages 563-574, September.
    9. William J. Browne & Ian L. Dryden & Kelly Handley & Shahid Mian & Dirk Schadendorf, 2010. "Mixed effect modelling of proteomic mass spectrometry data by using Gaussian mixtures," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(4), pages 617-633, August.
    10. Zhu, Hongxiao & Morris, Jeffrey S. & Wei, Fengrong & Cox, Dennis D., 2017. "Multivariate functional response regression, with application to fluorescence spectroscopy in a cervical pre-cancer study," Computational Statistics & Data Analysis, Elsevier, vol. 111(C), pages 88-101.
    11. Lin Zhang & Veerabhadran Baladandayuthapani & Hongxiao Zhu & Keith A. Baggerly & Tadeusz Majewski & Bogdan A. Czerniak & Jeffrey S. Morris, 2016. "Functional CAR Models for Large Spatially Correlated Functional Datasets," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 772-786, April.

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