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Creators/Authors contains: "Jurek, Marcin"

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  1. ; ( , Environmetrics)
    Accepted Manuscript Full Text Available
  2. ; ( , Statistics and Computing)
    Abstract

    Spatial statistics often involves Cholesky decomposition of covariance matrices. To ensure scalability to high dimensions, several recent approximations have assumed a sparse Cholesky factor of the precision matrix. We propose a hierarchical Vecchia approximation, whose conditional-independence assumptions imply sparsity in the Cholesky factors of both the precision and the covariance matrix. This remarkable property is crucial for applications to high-dimensional spatiotemporal filtering. We present a fast and simple algorithm to compute our hierarchical Vecchia approximation, and we provide extensions to nonlinear data assimilation with non-Gaussian data based on the Laplace approximation. In several numerical comparisons, including a filtering analysis of satellite data, our methods strongly outperformed alternative approaches.

     
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  3. ; ( , Journal of Computational and Graphical Statistics)
    Full Text Available