Degree Type
Creative Component
Semester of Graduation
Fall 2019
Department
Statistics
First Major Professor
Somak Dutta
Degree(s)
Master of Science (MS)
Major(s)
Statistics
Abstract
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 linearity selection, we use penalized thin plate spline basis functions and derive a penalized h-likelihood method for simultaneous non-linearity selection and spatial adjustments. We derive novel estimating equations for estimating the precision parameters based on the profile h-likelihood. We demonstrate our method using Arsenic contamination data from Bangladesh.
Copyright Owner
Hao, Sun
Copyright Year
2019
File Format
Recommended Citation
Sun, Hao, "A PENALIZED H-LIKELIHOOD METHOD FOR GAUSSIAN SPATIAL ADDITIVE MODEL ON REGULAR LATTICE" (2019). Creative Components. 461.
https://lib.dr.iastate.edu/creativecomponents/461
