SEMIPARAMETRIC INFERENCE VIA SPARSITY-INDUCED KRIGING FOR MASSIVE SPATIAL DATASETS
University of Cincinnati/SIParCS
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. This manuscript proposes 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.
Thursday, June 2, 2016
Mesa Lab, Damon Room
(Bring your lunch)