KAUST, Saudi Arabia
IST, Edifício da Matemática, sala P3.10
23 março 2023 (5.ª feira) – 15h:00m
Spatially misaligned data are becoming increasingly common due to advances in data collection and management. We present a Bayesian geostatistical model for the combination of data obtained at different spatial resolutions. The model assumes that underlying all observations, there is a spatially continuous variable that can be modeled using a Gaussian random field process. The model is fitted using the integrated nested Laplace approximation (INLA) and the stochastic partial differential equation (SPDE) approaches. In order to allow the combination of spatially misaligned data, a new SPDE projection matrix for mapping the Gaussian Markov random field from the observations to the triangulation nodes is proposed. We show the performance of the new approach by means of simulation and an application of PM2.5 prediction in USA. The approach presented provides a useful tool in a wide range of situations where information at different spatial scales needs to be combined.n this talk I will discuss the Bayes factor: What it is, why (or why not) it should be used, and how to use it. My emphasis will be more on conceptual understanding and less on technicalities, as much as possible. My talk will include both theoretical and practical features, hopefully catering for an informed use of the Bayes factor.
Prof. Paula Moraga is an Assistant Professor of Statistics at King Abdullah University of Science and Technology (KAUST), and the Principal Investigator of the GeoHealth group. Paula’s research focuses on the development of innovative statistical methods and computational tools for geospatial data analysis and health surveillance. She develops spatial and spatio-temporal statistical methods to understand the geographic and temporal patterns of diseases, assess their relationship with potential risk factors, detect clusters, measure inequalities, and evaluate the impact of interventions. She also works on the development of statistical software and interactive visualization applications for reproducible research and communication, and the impact of her work has directly informed strategic policy in reducing the burden of diseases such as malaria and cancer in several countries. She has published extensively in leading journals and is the author of the book “Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny” (2019, Chapman & Hall/CRC).
A joint CEAUL / CEMAT