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Activity Number: 173
Type: Contributed
Date/Time: Monday, August 1, 2016 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics and the Environment
Abstract #318680
Title: Semiparametric Inference via Sparsity-Induced Kriging for Massive Spatial Data Sets
Author(s): Pulong Ma* and Emily Lei Kang
Companies: University of Cincinnati and University of Cincinnati
Keywords: Semiparametric ; Spatial-random effect ; Basis function ; Sparsity
Abstract:

With the development of new remote sensing technology, large or even massive spatial datasets covering the globe become available. Statistical analysis of such data is challenging. We propose a semiparametric approach to modeling and inference for massive spatial datasets. In particular, a Gaussian process with additive components is considered, with its covariance structure coming from two components: one part is flexible without assuming a specific parametric covariance function but is able to achieve dimension reduction; the second part is parametric and simultaneously induces sparsity. The inference algorithm for parameter estimation and spatial prediction is devised. The resulting spatial prediction method that we call sparsity-induced kriging (SIK), is applied to simulated data and a massive satellite dataset. The results demonstrate the computational and inferential benefits of SIK over competing methods and show that SIK is more flexible and more robust against model misspecification.


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