Accommodating outlying observations in environmental spatio-temporal processes
Alexandra Schmidt, PhD
Professor of Biostatistics
Department of Epidemiology, Biostatistics and Occupational Health | McGill
Where: In-person | 2001 McGill College Avenue, room 1140;
Abstract
In the analysis of most spatiotemporal processes in environmental studies, observations present distributions that are not normal. Commonly, some transformation is applied to the data and inference is performed at the transformed scale. Commonly, the transformation will have an impact on the description of the uncertainty at future instants of time or unobserved locations of interest.
In this talk I will discuss some of the projects I have been involved with in the last five years that relax the assumption of normality of spatiotemporal processes after some suitable transformation of the data. In particular, I will focus on a recent proposal that models the variance law of multivariate dynamic linear models. The proposed approach adds flexibility to the usual Multivariate Dynamic Gaussian model by defining the process as a scale mixture between a Gaussian and log-Gaussian processes. The scale is represented by a process varying smoothly over space and time which is allowed to depend on covariates. Analysis of artificial datasets show that the parameters are identifiable and simpler models are well recovered by the general proposed model. The analyses of two important environmental processes, maximum temperature and maximum ozone, illustrate the effectiveness of our proposal in improving the uncertainty quantification in the prediction of spatio-temporal processes.
Speaker Bio
Alex Schmidt is Professor of Biostatistics and holds the endowed University Chair in the Department of Epidemiology, Biostatistics and Occupational Health (EBOH) at McGill University. She is an Elected Fellow of the American Statistical Association (2020) and an Elected Member of the International Statistical Institute (2010). She was awarded the Distinguished Achievement Medal (2017) from the American Statistical Association鈥檚 Section on Statistics and the Environment and the Abdel-ElShaarawi Young Investigator Award (2008), from The International Environmetrics Society.
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