{"id":2557,"date":"2025-11-05T22:41:37","date_gmt":"2025-11-06T01:41:37","guid":{"rendered":"https:\/\/www.ufrgs.br\/ppgest\/?p=2557"},"modified":"2025-11-05T22:42:34","modified_gmt":"2025-11-06T01:42:34","slug":"ciclo-de-seminarios-2025-tatiane-fontana-ribeiro-ufsm","status":"publish","type":"post","link":"https:\/\/www.ufrgs.br\/ppgest\/ciclo-de-seminarios-2025-tatiane-fontana-ribeiro-ufsm\/","title":{"rendered":"Ciclo de Semin\u00e1rios 2025 \u2013 Tatiane Fontana Ribeiro (UFSM)"},"content":{"rendered":"

Temos o prazer de convidar a todos para mais um semin\u00e1rio do Programa de P\u00f3s-Gradua\u00e7\u00e3o em Estat\u00edstica da UFRGS (PPGEst-UFRGS)<\/strong>.<\/p>\n

T\u00edtulo:<\/strong> Bivariate generalized autoregressive models for forecasting bivariate non-Gaussian times series<\/p>\n

Short Bio:<\/strong> Possui gradua\u00e7\u00e3o em Matem\u00e1tica (2017) pela Universidade Regional Integrada do Alto Uruguai e das Miss\u00f5es, com per\u00edodo sandu\u00edche na Universidade do Porto – Portugal. Possui especializa\u00e7\u00e3o em Estat\u00edstica e Modelagem Quantitativa (2019) pela Universidade Federal de Santa Maria (UFSM). Possui mestrado em Estat\u00edstica (2020) pela Universidade Federal de Pernambuco (UFPE). Possui gradua\u00e7\u00e3o em Estat\u00edstica (2023) pela UFSM. Possui doutorado em Estat\u00edstica pelo Instituto de Matem\u00e1tica e Estat\u00edstica da Universidade de S\u00e3o Paulo (IME-USP). Atualmente \u00e9 bolsista de p\u00f3s-doutorado no Programa de P\u00f3s-Gradua\u00e7\u00e3o em Matem\u00e1tica da UFSM, pela Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado do Rio Grande do Sul (FAPERGS), atuando no desenvolvimento de novos modelos para vari\u00e1veis aleat\u00f3rias com suporte duplamente limitado. Suas principais \u00e1reas de interesse e atua\u00e7\u00e3o s\u00e3o: gera\u00e7\u00e3o de distribui\u00e7\u00f5es de probabilidade, an\u00e1lise multivariada, modelos de regress\u00e3o e modelos de s\u00e9ries\u00a0temporais.<\/p>\n

Resumo:<\/strong> This paper introduces a novel approach, the bivariate generalized autoregressive (BGAR) model, for modeling and forecasting bivariate time series data. The BGAR model generalizes the bivariate vector autoregressive (VAR) models by allowing data that does not necessarily follow a normal distribution. We consider a random vector of two time series and assume each belongs to the canonical exponential family, similarly to the univariate generalized autoregressive moving average (GARMA) model. We include autoregressive terms of one series into the dynamical structure of the other and vice versa. The model parameters are estimated using the conditional maximum likelihood (CML) method. We provide general closed-form expressions for the conditional score vector and conditional Fisher information matrix, encompassing all canonical exponential family distributions. We develop asymptotic confidence intervals and hypothesis tests. We discuss techniques for model selection, residual diagnostic analysis, and forecasting. We carry out Monte Carlo simulation studies to evaluate the performance of the finite sample CML inferences, including point and interval estimation. An application to real data analyzes the number of leptospirosis cases on hospitalizations due to leptospirosis in S\u00e3o Paulo state, Brazil. Competing models such as GARMA, autoregressive integrated moving average (ARIMA), and VAR models are considered for comparison purposes. The new model outperforms the competing models by providing more accurate out-of-sample forecasting and allowing quantification of the lagged effect of the case count series on hospitalizations due to leptospirosis.<\/p>\n

Data: 11\/11\/2025
\nHor\u00e1rio: 13:30<\/strong><\/p>\n

O semin\u00e1rio acontecer\u00e1 na<\/span>\u00a0modalidade Online<\/strong>\u00a0e poder\u00e1 ser assistido pelo link: https:\/\/mconf.ufrgs.br\/webconf\/ppgest<\/span><\/p>\n