[ABE-L] Fwd: Seminários - Temático FAPESP

[Márcia Branco]

---------- Forwarded message ----------
From: Márcia Branco <mbranco em ime.usp.br>
Date: Mon, Aug 7, 2017 at 10:53 AM
Subject: Re: Seminários - Temático FAPESP
To: professores em ime.usp.br, g-posmae em ime.usp.br, g-posmat em ime.usp.br,
g-posmac em ime.usp.br, g-posmap em ime.usp.br


Lembrando, amanhã(terça) a partir das 14h, palestras do professor Marc
Genton (https://www.kaust.edu.sa/en/study/faculty/marc-genton)
e professora Ying Sun ( https://www.kaust.edu.sa/en/study/faculty/ying-sun)
.

Márcia.

On Wed, Aug 2, 2017 at 9:13 AM, Márcia Branco <mbranco em ime.usp.br> wrote:

> 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.
> ------------------------------------------------------------
> -----------------------------------------
>
> *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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