Fast Maximum Likelihood Inference for Spatial Generalized Linear Mixed Models

• Activity Number: 678 - New Methods in Spatial and Spatiotemporal Modeling and Assessment
• Type: Contributed
• Date/Time: Thursday, August 2, 2018 : 10:30 AM to 12:20 PM
• Sponsor: Section on Statistics and the Environment
• Author(s): Yawen Guan* and Murali Haran
• Companies: The Statistical and Applied Mathematical Sciences Institute and Penn State University
• Keywords: Monte Carlo Expectation Maximization; Projection-Based Models; Spatial Non-Gaussian Data; spatial generalized linear mixed model

Abstract

Spatial generalized linear mixed models (SGLMMs) are popular and flexible models for spatial non-Gaussian data. They are useful for spatial interpolations as well as for fitting regression models that account for spatial dependence, and are commonly used in many disciplines such as epidemiology, atmospheric science, and sociology. Inference for SGLMMs is typically carried out under the Bayesian framework. Maximum likelihood inference is also available but computational issues often make it problematic, especially when high-dimensional spatial data are involved. Here we provide a computationally efficient projection-based maximum likelihood approach for routinely fitting SGLMMs. Our methodology is very general and applies to both discrete-domain (Gaussian Markov random field) as well as continuous-domain (Gaussian process) spatial models.