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Hierarchical Bayesian model for predicting spatially correlated curves and with non-equidistant spacing

Grant number: 23/18036-3
Support Opportunities:Scholarships in Brazil - Post-Doctoral
Start date: August 01, 2024
End date: July 31, 2026
Field of knowledge:Physical Sciences and Mathematics - Probability and Statistics - Statistics
Principal Investigator:Luiz Koodi Hotta
Grantee:Alvaro Alexander Burbano Moreno
Host Institution: Instituto de Matemática, Estatística e Computação Científica (IMECC). Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
Associated research grant:23/02538-0 - Time series, wavelets, high dimensional data and applications, AP.TEM

Abstract

The primary objective of this research project is to build upon and enhance the findings of Burbano (2023), who introduced a Gaussian functional Bayesian model incorporating B-spline smoothing techniques, Bernstein polynomials, and an autoregressive random effect component. Burbano's model is particularly adept at addressing irregular spacing between data points in each series. In this study, we aim to extend and refine the existing model, detailing these extensions comprehensively within the research project. Subsequently, we intend to apply these enhanced models to a set of real-world data, leveraging them to make predictions in unobserved locations. (AU)

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