Online Program Home
My Program

Abstract Details

Activity Number: 121 - Handling Large Dimensionality, Skewness and Non-Stationarity Through Multi-Resolution Spatial Modeling
Type: Topic Contributed
Date/Time: Monday, July 29, 2019 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics and the Environment
Abstract #302939
Title: Models for Large Multivariate Spatial Data
Author(s): Soutir Bandyopadhyay*
Companies: Colorado School of Mines
Keywords: Coherence; Sparse; Multiresolution

Multivariate spatial modeling is a rapidly growing field, but most extant models are infeasible for use with massive spatial processes. In this work we introduced a highly flexible, interpretable and scalable multiresolution approach to multivariate spatial modeling. Relying on compactly supported basis functions and Gaussian Markov random field specifications for coefficients results in efficient and scalable calculation routines for likelihood evaluations and cokriging. We analytically show that special parameterizations approximate popular existing models. Moreover, the multiresolution approach allows for arbitrary specification of scale dependence between processes. We illustrate our approach through Monte Carlo studies to illustrate implied stochastic behavior and test our ability to recover scale dependence, and moreover examine a complex large bivariate observational minimum and maximum temperature dataset over the western United States.

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

Back to the full JSM 2019 program