Colloquium - Andrew Finley

Institution: Michigan State University
Title: Tackling Large Spatial Datasets via Dimension Reduction, Induced Sparsity, and Distributed Computing: A Case Study in Forestry Applications
Date: May 4, 2023
Location: C405 Wells Hall, Simulcast to Zoom (Click here for meeting details)
Time: 10:20 AM - 11:10 AM Eastern Time

Abstract:
Spatial process models for analyzing geostatistical data entail computations that become prohibitive as the number of spatial locations increases. Developing statistical and computational methods to tackle this challenge of dimensionality is an active area of research. In practice, workable solutions commonly require a combination of complementary modeling and computing tools. This talk highlights a class of highly scalable Nearest Neighbor Gaussian Process (NNGP) models that provide fully model-based inference for large geostatistical datasets. Presentation of the NNGP is motivated by a study that aims to predict forest biomass across the remote Tanana Inventory Unit (TIU) in interior Alaska. A two-stage hierarchical Bayesian model is proposed to estimate forest biomass density and total given sparsely sampled remotely sensed data and georeferenced forest inventory plot measurements. The model is motivated by the United States Department of Agriculture (USDA) Forest Service Forest Inventory and Analysis (FIA) objective to provide biomass estimates for the TIU. The proposed model yields stratum-level biomass estimates for arbitrarily sized areas of interest. Model-based estimates are compared with the TIU FIA design-based post-stratified estimates. Model-based small area estimates (SAEs) for two experimental forests within the TIU are compared with each forest's design-based estimates generated using a dense network of independent inventory plots. Results support a model-based approach to estimating forest parameters when inventory data are sparse or resources limit collection of enough data to achieve desired accuracy and precision using design-based methods.

Bio:
Andrew O. Finley is a professor at Michigan State University with a joint appointment in the Departments of Forestry and Geography, and is adjunct in the Department of Statistics & Probability. He is also a member of the interdisciplinary Ecology, Evolutionary Biology, and Behavior Graduate Program faculty. His research interests lie in developing methodologies for monitoring and modeling environmental processes, Bayesian statistics, spatial statistics, and statistical computing. A central theme in his research is the use of hierarchical models to integrate information from disparate sources to improve inference and prediction. In terms of application areas, his research focuses on spatial-temporal modeling of changing ecosystem components and systems. His recent interest is in improving frameworks for modeling exposure to pollutants, climate change, and health outcomes (ecosystem and public).