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Universidade Estadual de Campinas (UNICAMP). Instituto de Matemática, Estatística e Computação Científica (IMECC) (Institutional affiliation from the last research proposal) Birthplace: Brazil
Marcos Prates obtained his bachelor's in 2006 in the Computational Mathematics program at the Universidade Federal de Minas Gerais (UFMG) and a master's in Statistics in 2008 from the same institution. In 2011 he received his Ph.D. in Statistics from the University of Connecticut and was a Visiting Professor in the same university from 2019 to 2020. Currently, he is an Associate Professor at UFMG. His main research areas are Bayesian Statistics, Generalized Linear Mixed Models, Machine Learning, and Spatial Statistics. He was Director of the Graduate Program in Statistics in UFMG (2016-2018), was the Secretary (2015-2016) and Thresury (2023-2024) for ISBRA, the Brazilian chapter of ISBA, also, was the President of the Brazilian Statistical Association (2020-2022). (Source: Lattes Curriculum)
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To assume that observations follow a normal distribution (or symmetric) is a common assumption in models with spatial structure. However, this assumption can be unrealistic, hidding important characteristics of the underlying variation present in the data. Kim and Mallick (2004) and Hosseini, Eidsvik and Mohammadzadeh (2011) show through simulation that a misspecification of the distribut…
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