Title: Functional data analysis: spatial association of curves and irregular spacing.
Wednesdays, September 18, from 1:00 p.m. to 2:30 p.m. (Rio de Janeiro local time)
Speaker: Vinicius Diniz Mayrink (UFMG)
Local: Sala C116 do Instituto de Matemática, Centro de Tecnologia - UFRJ
Abstract: Spatial Functional Data (SFD) analysis is an emerging statistical framework that combines Functional Data Analysis (FDA) and spatial dependency modeling. Unlike traditional statistical methods, which treat data as scalar values or vectors, SFD considers data as continuous functions, allowing for a more comprehensive understanding of their behavior and variability. This approach is well-suited for analyzing data collected over time, space, or any other continuous domain. SFD has found applications in various fields, including economics, finance, medicine, environmental science, and engineering. This study proposes new functional Gaussian models incorporating spatial dependence structures, focusing on irregularly spaced data and reflecting spatially correlated curves. The model is based on Bernstein polynomial (BP) basis functions and utilizes a Bayesian approach for estimating unknown quantities and parameters. The paper explores the advantages and limitations of the BP model in capturing complex shapes and patterns while ensuring numerical stability. The main contributions of this work include the development of an innovative model designed for SFD using BP, the presence of a random effect to address associations between irregularly spaced observations, and a comprehensive simulation study to evaluate models’ performance under various scenarios. The work also presents one real application of Temperature in Mexico City, showcasing practical illustrations of the proposed model.
Mais informações podem ser encontradas no site: https://www.dme.ufrj.br/
Title: Limit theorems for chaotic dynamical systems preserving an infnite measure
Speaker: (Francoise Pène, UBrest)
Semptember 28, at 11:00 a.m. (Rio de Janeiro local time)
Local: C116 - Bloco C - CT – Instituto de Matemática – UFRJ.
Abstract: We are interested in the ergodic and stochastic properties of dynamical systems preserving a infinite (sigma-finite) measure. We will investigate the notions of recurrence, ergodicity, mixing rate, law of large numbers, central limit theorems in this context, with illustrations on natural examples (billiards, intermittent maps). We will see strategies to establish these results using probability preserving dynamical systems (either via Z^d-extension or via induction).
Contato: gelfert@im.ufrj.br
Título: Encontro de Egressos PPGE em Estatística
Data: 10 e 11 de outubro de 2024
Comissão Científica: Maria Eulalia, Glauco Valle, Guilherme Ost e João Batista M. Pereira.
Contato: coord.ppge@im.ufrj.br
Interessados em enviar trabalhos devem enviar título e resumo até o dia 21 de setembro para o email indicado.
Title: Lapse risk modelling in insurance: a Bayesian mixture approach
Wednesdays, September 4, from 3:30 p.m. to 5:00 p.m. (Rio de Janeiro local time)
Speaker: Viviana Lobo (IM-UFRJ)
Local: Laboratório de Sistemas Estocásticos (LSE), Sala I-044-B, Centro de Tecnologia - UFRJ
Abstract: This paper focuses on modelling surrender time for policyholders in the context of life insurance. In this setup, a large lapse rate at the first months of a contract is often observed, with a decrease in this rate after some months. The modelling of the time to cancellation must account for this specific behaviour. Another stylised fact is that policies which are not cancelled in the study period are considered censored. To account for both censoring and heterogeneous lapse rates, this work assumes a Bayesian survival model with a mixture of regressions. The inference is based on data augmentation allowing for fast computations even for datasets of over millions of clients. An illustrative example emulates a typical behaviour for life insurance contracts and a simulated study investigates the properties of the proposed model. A case study is considered and illustrates the flexibility of our proposed model allowing different specifications of mixture components In particular, the observed censoring in the insurance context might be up to 50% of the data, which is very unusual for survival models in other fields such as epidemiology. This aspect is exploited in our simulated study.
For more information, click here: https://www.dme.ufrj.br/
