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  1. ; ; (, Biometrics)
    ABSTRACT Motivated by the need for computationally tractable spatial methods in neuroimaging studies, we develop a distributed and integrated framework for estimation and inference of Gaussian process model parameters with ultra-high-dimensional likelihoods. We propose a shift in viewpoint from whole to local data perspectives that is rooted in distributed model building and integrated estimation and inference. The framework’s backbone is a computationally and statistically efficient integration procedure that simultaneously incorporates dependence within and between spatial resolutions in a recursively partitioned spatial domain. Statistical and computational properties of our distributed approach are investigated theoretically and in simulations. The proposed approach is used to extract new insights into autism spectrum disorder from the autism brain imaging data exchange. 
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  2. ; ; (, Technometrics)
    Free, publicly-accessible full text available March 10, 2026
  3. ; ; ; (, Bayesian Analysis)
    Free, publicly-accessible full text available January 1, 2026
  4. ; ; (, The Annals of Applied Statistics)
    Full Text Available 
  5. ; ; (, The Annals of Applied Statistics)
    Full Text Available 
  6. ; (, Spatial Statistics)
    Full Text Available 
  7. ; (, Journal of the American Statistical Association)
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  8. ; ; (, Journal of the American Statistical Association)
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