[ABE-L] Seminários - IME-USP - Temático Fapesp

[Márcia Branco]

Caros

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.

Saudações

Márcia.
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*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*.
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.

*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.
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