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More nonparametric Bayesian inference in applications

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  • Michele Guindani

    (University of California)

  • Wesley O. Johnson

    (University of California)

Abstract

Discussion of “Nonparametric Bayesian Inference in Applications” by Peter Mueller, Fernando A. Quintana, Garritt Page: More Nonparametric Bayesian Inference in Applications.

Suggested Citation

  • Michele Guindani & Wesley O. Johnson, 2018. "More nonparametric Bayesian inference in applications," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(2), pages 239-251, June.
    • Handle: RePEc:spr:stmapp:v:27:y:2018:i:2:d:10.1007_s10260-017-0399-6
    DOI: 10.1007/s10260-017-0399-6
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    References listed on IDEAS

    as
    1. Michele Guindani & Peter Müller & Song Zhang, 2009. "A Bayesian discovery procedure," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(5), pages 905-925, November.
    2. Nancy Flournoy & Caterina May & Piercesare Secchi, 2012. "Asymptotically Optimal Response-Adaptive Designs for Allocating the Best Treatment: An Overview," International Statistical Review, International Statistical Institute, vol. 80(2), pages 293-305, August.
    3. Wenguang Sun & Brian J. Reich & T. Tony Cai & Michele Guindani & Armin Schwartzman, 2015. "False discovery control in large-scale spatial multiple testing," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 77(1), pages 59-83, January.
    4. 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.
    5. Maria De Iorio & Wesley O. Johnson & Peter Müller & Gary L. Rosner, 2009. "Bayesian Nonparametric Nonproportional Hazards Survival Modeling," Biometrics, The International Biometric Society, vol. 65(3), pages 762-771, September.
    6. Michele Guindani & Nuno Sepúlveda & Carlos Daniel Paulino & Peter Müller, 2014. "A Bayesian semiparametric approach for the differential analysis of sequence counts data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 63(3), pages 385-404, April.
    7. Gelfand, Alan E. & Kottas, Athanasios & MacEachern, Steven N., 2005. "Bayesian Nonparametric Spatial Modeling With Dirichlet Process Mixing," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1021-1035, September.
    8. Kim‐Anh Do & Peter Müller & Feng Tang, 2005. "A Bayesian mixture model for differential gene expression," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(3), pages 627-644, June.
    9. Efron, Bradley, 2004. "Large-Scale Simultaneous Hypothesis Testing: The Choice of a Null Hypothesis," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 96-104, January.
    10. Yisheng Li & Xihong Lin & Peter Müller, 2010. "Bayesian Inference in Semiparametric Mixed Models for Longitudinal Data," Biometrics, The International Biometric Society, vol. 66(1), pages 70-78, March.
    11. Peter Muller & Giovanni Parmigiani & Christian Robert & Judith Rousseau, 2004. "Optimal Sample Size for Multiple Testing: The Case of Gene Expression Microarrays," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 990-1001, December.
    12. Edoardo M. Airoldi & Thiago Costa & Federico Bassetti & Fabrizio Leisen & Michele Guindani, 2014. "Generalized Species Sampling Priors With Latent Beta Reinforcements," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 109(508), pages 1466-1480, December.
    13. Fernando A. Quintana & Wesley O. Johnson & L. Elaine Waetjen & Ellen B. Gold, 2016. "Bayesian Nonparametric Longitudinal Data Analysis," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(515), pages 1168-1181, July.
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    2. Banerjee, Sayantan & Akbani, Rehan & Baladandayuthapani, Veerabhadran, 2019. "Spectral clustering via sparse graph structure learning with application to proteomic signaling networks in cancer," Computational Statistics & Data Analysis, Elsevier, vol. 132(C), pages 46-69.

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