About Matheus B. Guerrero Matheus B. Guerrero Ph.D. Student, Statistics extSTAT Research Group Time Series extreme-value theory Matheus was a Ph.D. student in Statistics at the King Abdullah University of Science and Technology (KAUST), doing research under the joint supervision of Prof. Raphaël Huser and Prof. Hernando Ombao. Matheus successfully defended his PhD thesis entitled " Modeling and Inference for Multivariate Time Series, with Applications to Integer-Valued Processes and Nonstationary Extreme Data" on April 4th, 2023; see his PhD thesis here. His PhD committee was composed of Professors Raphaël Huser (chair), Prof. Hernando Ombao (co-chair), Miguel de Carvalho (external examiner from the University of Events Presented Events Apr 2 - Apr 8, 2023 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.
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.
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