statistical methods

Geospatial Data Science for Public Health Surveillance

Paula Moraga, Assistant Professor, Statistics

Nov 20, 11:00 - 12:00

B9 L2 R2325

statistical methods geospatial data analysis health surveillance Public Health spatio-temporal data analysis

This talk will give an overview of statistical methods and computational tools for geospatial data analysis and health surveillance, highlighting challenges related to data biases and availability.

Modeling and Inference for Multivariate Time Series, with Applications to Integer-Valued Processes and Nonstationary Extreme Data

Matheus B. Guerrero, Ph.D. Student, Statistics

Apr 4, 16:00 - 19:00

B4 L5 R5220

statistical methods integer-valued data autoregressive processes multivariate nonstationary extreme data

This Ph.D. research focuses on proposing new statistical methods for two types of time series data: integer-valued data and multivariate nonstationary extreme data. For the former, the researcher proposes a novel approach to building an integer-valued autoregressive (INAR) model that offers the flexibility to specify both marginal and innovation distributions, leading to several new INAR processes. For the latter, the researcher proposes new extreme value theory methods for analyzing multivariate nonstationary extreme data, specifically EEG recordings from patients with epilepsy. Two extreme-value methods, Conex-Connect and Club Exco, are proposed to study alterations in the brain network during extreme events such as epileptic seizures.