<html><head></head><body><div class="yahoo-style-wrap" style="font-family:Helvetica Neue, Helvetica, Arial, sans-serif;font-size:16px;"><div><div><span style="font-family: Arial, Helvetica, sans-serif; font-size: small; color: rgb(34, 34, 34);">Boa tarde,</span><br></div><div><div style="color: rgb(34, 34, 34); font-family: Roboto, RobotoDraft, Helvetica, Arial, sans-serif; font-size: medium;"><div class="ydp45b7be32ii ydp45b7be32gt" style="font-size: 12.8px; direction: ltr; margin: 8px 0px 0px; padding: 0px; position: relative;"><div class="ydp45b7be32a3s ydp45b7be32aXjCH" style="font-stretch: normal; font-size: small; line-height: 1.5; font-family: Arial, Helvetica, sans-serif;"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div dir="ltr"><div><br></div><div>Convido a todos a participarem do próximo <span class="ydp45b7be32m_-2157026724991549056gmail-il">Papos</span> <span class="ydp45b7be32m_-2157026724991549056gmail-il">Aleatórios</span> que ocorrerá dia 24 de maio de 2019, sexta, às 14h30m no Gragoatá e será conduzido pela professora Viviana G. R. Lobo da Universidade Federal do Rio de Janeiro.</div><div dir="ltr"><br></div><div>Local: UFF - Gragoatá - IME - bloco G - sala 205</div><div><br></div><div>Título: Bayesian residual analysis for spatially correlated data</div><div><br></div>Resumo:</div><div dir="ltr">This work considers residual analysis and predictive techniques in the identication of individual and multiple outliers in geostatistical data. The standardized Bayesian spatial residual is proposed and computed for three competing models: the Gaussian, Student-t and Gaussian-log-Gaussian spatial processes. In this context, the spatial models are investigated regarding their plausibility for datasets contaminated with outliers. The posterior probability of an outlying observation is computed based on the standardized residuals and different thresholds for outlier discrimination are tested. From a predictive point of view, methods such as the conditional predictive ordinate, the predictive concordance and the Savage-Dickey density ratio for hypothesis testing are investigated in the identication of outliers in the spatial setting. For illustration, contaminated datasets are considered to access the performance of the three spatial models in the identication of outliers in spatial data. Furthermore, an application to wind speed modelling is presented to illustrate the usefulness of the proposed tools in the detection of regions with large wind speeds. Based on joint work with Thais C. O. Fonseca (IM-UFRJ).</div><div dir="ltr"><div dir="ltr" class="ydp45b7be32m_-2157026724991549056gmail_signature"><div dir="ltr"><div dir="ltr"><div style="font-size: 12.8px;"><br></div><div style="font-size: 12.8px;"><div style="font-size: small;">Maiores informações podem ser obtidas em <a href="https://sites.google.com/view/paposaleatorios" data-saferedirecturl="https://www.google.com/url?q=https://sites.google.com/view/paposaleatorios&source=gmail&ust=1558039157018000&usg=AFQjCNFNVDI_MWaMbvSX3USEZk-DRSRzKQ" style="color: rgb(17, 85, 204);" rel="nofollow" target="_blank">https://sites.google.com/view/paposaleatorios</a> .</div><br class="ydp45b7be32m_-2157026724991549056gmail-Apple-interchange-newline"></div><div style="font-size: 12.8px;">Att,</div><div style="font-size: 12.8px;"> </div><div style="font-size: 12.8px;">Patrícia Lusié</div><div style="font-size: 12.8px;">Professora Adjunta</div><div style="font-size: 12.8px;">Departamento de Estatística</div><div style="font-size: 12.8px;">Instituto de Matemática e Estatística - UFF</div></div></div></div></div></div></div></div></div></div></div></div></div><br></div></body></html>