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Publications (2)3.8 Total impact

  • Jenný Brynjarsdóttir, L. Mark Berliner
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    ABSTRACT: The field of spatial and spatio-temporal statistics is increasingly faced with the challenge of very large datasets. The classical approach to spatial and spatio-temporal modeling is very computationally demanding when datasets are large, which has led to interest in methods that use dimension-reduction techniques. In this article, we focus on modeling of two spatio-temporal processes where the primary goal is to predict one process from the other and where datasets for both processes are large. We outline a general dimension-reduced Bayesian hierarchical modeling approach where spatial structures of both processes are modeled in terms of a low number of basis vectors, hence reducing the spatial dimension of the problem. Temporal evolution of the processes and their dependence is then modeled through the coefficients of the basis vectors. We present a new method of obtaining data-dependent basis vectors, which is geared toward the goal of predicting one process from the other. We apply these methods to a statistical downscaling example, where surface temperatures on a coarse grid over Antarctica are downscaled onto a finer grid. Supplementary materials for this article are available online.
    Journal of the American Statistical Association 10/2014; 109(508). · 2.11 Impact Factor
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    Jenný Brynjarsdóttir, L. Mark Berliner
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    ABSTRACT: We present a Bayesian hierarchical modeling approach to paleoclimate reconstruction using borehole temperature profiles. The approach relies on modeling heat conduction in solids via the heat equation with step function, surface boundary conditions. Our analysis includes model error and assumes that the boundary conditions are random processes. The formulation also enables separation of measurement error and model error. We apply the analysis to data from nine borehole temperature records from the San Rafael region in Utah. We produce ground surface temperature histories with uncertainty estimates for the past 400 years. We pay special attention to use of prior parameter models that illustrate borrowing strength in a combined analysis for all nine boreholes. In addition, we review selected sensitivity analyses.
    The Annals of Applied Statistics 07/2011; 5(2011). · 1.69 Impact Factor