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Hierarchical multiresolution approaches for dense point-level breast cancer treatment data

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  • Liang, Shengde
  • Banerjee, Sudipto
  • Bushhouse, Sally
  • Finley, Andrew O.
  • Carlin, Bradley P.

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  • Liang, Shengde & Banerjee, Sudipto & Bushhouse, Sally & Finley, Andrew O. & Carlin, Bradley P., 2008. "Hierarchical multiresolution approaches for dense point-level breast cancer treatment data," Computational Statistics & Data Analysis, Elsevier, vol. 52(5), pages 2650-2668, January.
  • Handle: RePEc:eee:csdana:v:52:y:2008:i:5:p:2650-2668
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    References listed on IDEAS

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    1. 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.
    2. Fuentes, Montserrat, 2007. "Approximate Likelihood for Large Irregularly Spaced Spatial Data," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 321-331, March.
    3. David J. Spiegelhalter & Nicola G. Best & Bradley P. Carlin & Angelika Van Der Linde, 2002. "Bayesian measures of model complexity and fit," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(4), pages 583-639, October.
    4. Paciorek, Christopher J., 2007. "Computational techniques for spatial logistic regression with large data sets," Computational Statistics & Data Analysis, Elsevier, vol. 51(8), pages 3631-3653, May.
    5. Montserrat Fuentes, 2002. "Spectral methods for nonstationary spatial processes," Biometrika, Biometrika Trust, vol. 89(1), pages 197-210, March.
    6. E. E. Kammann & M. P. Wand, 2003. "Geoadditive models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 52(1), pages 1-18, January.
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    Cited by:

    1. Edler, Lutz & Lee, Jae Won & Mittlböck, Martina & Niland, Joyce & Victor, Norbert, 2009. "Computational statistics within clinical research," Computational Statistics & Data Analysis, Elsevier, vol. 53(3), pages 583-585, January.

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