<div dir="ltr"><div>Caros</div><div> </div><div>Convido a todos para as primeiras apresentações do ciclo de seminários do grupo ModReg-Fapesp do segundo semestre de 2017 no IME-USP, conforme indicado abaixo. </div><div> </div><div>Saudações</div><div> </div><div>Márcia. </div><div>-----------------------------------------------------------------------------------------------------</div>Seminário - Projeto Temático: Modelos de Regressão e Aplicações Título: Directional Outlyingness for Multivariate Functional Data Palestrante: Marc G. Genton, King Abdullah University of Science and Technology (KAUST), Saudi Arabia Quando: 8 de agosto de 2017, terça-feira, às 14h. Onde: Auditório Antônio Gilioli - Sala 262 – Bloco A, segundo andar - IME-USP Resumo.<div>The direction of outlyingness is crucial to describing the centrality of multivariate functional data. Motivated by this idea, we generalize classical depth to directional outlyingness for functional data. We investigate theoretical properties of functional directional outlyingness and find that it naturally decomposes functional outlyingness into two parts: magnitude outlyingness and shape outlyingness which represent the centrality of a curve for magnitude and shape, respectively. Using this decomposition, we provide a visualization tool for the centrality of curves. Furthermore, we design an outlier detection procedure based on functional directional outlyingness. This criterion applies to both univariate and multivariate curves and simulation studies show that it outperforms competing methods. Weather and electrocardiogram data demonstrate the practical application of our proposed framework. We further discuss an outlyingness matrix for multivariate functional data classification as well as plots for multivariate functional data visualization and outlier detection. The talk is based on joint work with Wenlin Dai. </div><div> </div><div>Título: Visualization and Assessment of Spatio-temporal Covariance Properties Palestrante: Ying Sun, King Abdullah University of Science and Technology (KAUST), Saudi Arabia Quando: 8 de agosto de 2017, terça-feira, às 14h45min. Onde: Auditório Antônio Gilioli - Sala 262 – Bloco A, segundo andar - IME-USP Resumo.Spatio-temporal covariances are important for describing the spatio-temporal variability of underlying random processes in geostatistical data. For second-order stationary processes, there exist subclasses of covariance functions that assume a simpler spatio-temporal dependence structure with separability and full symmetry. However, it is challenging to visualize and assess separability and full symmetry from spatio-temporal observations. In this work, we propose a functional data analysis approach that constructs test functions using the cross-covariances from time series observed at each pair of spatial locations. These test functions of temporal lags summarize the properties of separability or symmetry for the given spatial pairs. We use functional boxplots to visualize the functional median and the variability of the test functions, where the extent of departure from zero at all temporal lags indicates the degree of non-separability or asymmetry. We also develop a rank-based nonparametric testing procedure for assessing the significance of the non-separability or asymmetry. The performances of the proposed methods are examined by simulations with various commonly used spatio-temporal covariance models. To illustrate our methods in practical applications, we apply it to real datasets, including weather station data and climate model outputs.</div><div> </div></div>