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Discussion of paper “nonparametric Bayesian inference in applications” by Peter Müller, Fernando A. Quintana and Garritt L. Page

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  • Athanasios Kottas

    (University of California)

Abstract

This is an invited discussion of review paper “Nonparametric Bayesian Inference in Applications” by Peter Müller, Fernando A. Quintana and Garritt L. Page.

Suggested Citation

  • Athanasios Kottas, 2018. "Discussion of paper “nonparametric Bayesian inference in applications” by Peter Müller, Fernando A. Quintana and Garritt L. Page," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 27(2), pages 219-225, June.
    • Handle: RePEc:spr:stmapp:v:27:y:2018:i:2:d:10.1007_s10260-017-0398-7
    DOI: 10.1007/s10260-017-0398-7
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    References listed on IDEAS

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    1. Athanasios Kottas & Sam Behseta, 2010. "Bayesian Nonparametric Modeling for Comparison of Single-Neuron Firing Intensities," Biometrics, The International Biometric Society, vol. 66(1), pages 277-286, March.
    2. Patrick E. Brown & Gareth O. Roberts & Kjetil F. Kåresen & Stefano Tonellato, 2000. "Blur‐generated non‐separable space–time models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(4), pages 847-860.
    3. Anders Brix & Peter J. Diggle, 2001. "Spatiotemporal prediction for log‐Gaussian Cox processes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(4), pages 823-841.
    4. 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.
    5. Ishwaran, Hemant & James, Lancelot F., 2004. "Computational Methods for Multiplicative Intensity Models Using Weighted Gamma Processes: Proportional Hazards, Marked Point Processes, and Panel Count Data," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 175-190, January.
    6. Taddy, Matthew A., 2010. "Autoregressive Mixture Models for Dynamic Spatial Poisson Processes: Application to Tracking Intensity of Violent Crime," Journal of the American Statistical Association, American Statistical Association, vol. 105(492), pages 1403-1417.
    7. Xu, Ke & Wikle, Christopher K. & Fox, Neil I., 2005. "A Kernel-Based Spatio-Temporal Dynamical Model for Nowcasting Weather Radar Reflectivities," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 1133-1144, December.
    8. Anders Brix & Jesper Moller, 2001. "Space‐time Multi Type Log Gaussian Cox Processes with a View to Modelling Weeds," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 28(3), pages 471-488, September.
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