Note: This seminar will be via Zoom only!  Zoom Link: https://ucsb.zoom.us/j/82142702291

Compositionally-warped additive mixed modeling for a wide variety of non-Gaussian spatial data

Professor Daisuke Murakami

As with the advancement of geographical information systems, non-Gaussian spatial data sets are getting larger and more diverse. This study develops a general framework for fast and flexible non-Gaussian regression, especially for spatial/spatiotemporal modeling. The developed model, termed the compositionally-warped additive mixed model (CAMM), combines an additive mixed model (AMM) and the compositionally-warped Gaussian process to model a wide variety of non-Gaussian continuous data including spatial and other effects. A specific advantage of the proposed CAMM is that it requires no explicit assumption of data distribution unlike existing AMMs. Monte Carlo experiments show the estimation accuracy and computational efficiency of CAMM for modeling non-Gaussian data including fat-tailed and/or skewed distributions. The model is applied to crime data to examine the empirical performance of the regression analysis and prediction. The result shows that CAMM provides reasonable coefficient estimates and outperforms AMM in terms of prediction accuracy. CAMM is verified to be a fast and flexible model that potentially covers a wide variety of non-Gaussian data modeling. The proposed approach is implemented in an R package spmoran.

Biography:
DAISUKE MURAKAMI received his Ph.D. degree in engineering from University of Tsukuba in 2014. From 2014 to 2017, he worked at the National Institute for Environmental Studies, Japan as a research associate. Since 2017, he is working at the Department of Statistical Data Science, in the Institute of Statistical Mathematics, Japan, as an assistant professor, and as an associate professor since 2023. His research interests include spatial and spatiotemporal statistics, quantitative geography, urban and environmental analysis.