Palestrante: Guilherme Ludwig (UNICAMP)

Título: A non-stationary spatio-temporal covariance model with advection effects for rainfall data

Resumo: We propose a non-stationary model constructed using a mixture of spatio-temporal covariance models with advection effects (Gupta and Waymire, 1987; Cox and Isham, 1988); namely, models that have larger covariance values along an orientation vector in the spatio-temporal index set, that simulate wind direction and cloud movement. We show that a mixture of such models can allow for wind direction change in data during (estimated) time intervals, unlike classical models that use rigid advection effects. We construct a MCMC procedure for Bayesian estimation, and illustrate the problem with rainfall data from the southeastern region of Brazil. This is a joint work with Pedro Nasevicius Ramos (UNICAMP).

Mais informações: https://ppge.im.ufrj.br/ciclo-de-palestras-segundo-semestre-de-2025/

Organizadores: Maria Eulalia Vares e Widemberg S Nobre