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Activity Number: 190 - Contributed Poster Presentations: Section on Statistics and the Environment
Type: Contributed
Date/Time: Monday, July 29, 2019 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistics and the Environment
Abstract #305073
Title: A Penalized H-Likelihood Method for Gaussian Spatial Additive Model on Regular Lattice
Author(s): Hao Sun* and Somak Dutta
Companies: Iowa State University and Iowa State University
Keywords: additive mixed model; smoothing spline; sparse group lasso; orthogonal basis function; h-likelihood; Arsenic contamination

Often in spatial regression problems, the covariates could be high-dimensional and have a non-linear relationship with the response. Furthermore, the functional relationship between the response and the covariates are often smoother than the spatial correlation. We propose a Gaussian spatial additive model on regular lattice where the large scale effects of the spatial covariates are modeled by smooth functions and the small scale spatial variability is modeled using a random field on the lattice. In order to facilitate variable selection, we impose sparse group lasso penalty on the smooth functions and derive a penalized h-likelihood method for simultaneous model selection and spatial adjustments. We derive novel estimating equations for estimating the precision parameters based on the profiled h-likelihood. We demonstrate our method using Arsenic contamination data from Bangladesh.

Authors who are presenting talks have a * after their name.

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