<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"> <html><body style='font-size: 10pt; font-family: Verdana,Geneva,sans-serif'> <p>Rafael,</p> <p> Seria possível obter uma cópia da apresentação do prof. Enrico?</p> <p> Obrigado,</p> <p>Dalton Andrade</p> <p>INE/UFSC</p> <p> Em 19/05/2015 9:51, Rafael Izbicki escreveu:</p> <blockquote type="cite" style="padding-left:5px; border-left:#1010ff 2px solid; margin-left:5px"> <div dir="ltr"> <div style="font-size: 12.8000001907349px;"><span style="font-family: arial, helvetica, sans-serif;"><span style="color: #000000;">Divulgação da palestra desta semana do Seminário do </span><a href="http://www.icmc.usp.br/Portal/conteudo/1096/13/interinstitucional-de-pos-graduacao-em-estatistica"><span style="color: #e74e37;">Programa Interinstitucional de Pós-graduação em Estatística</span></a><span style="color: #000000;"> (PIPGEs ICMC/USP e UFSCar), São Carlos.</span></span></div> <div style="font-size: 12.8000001907349px;"><span style="font-family: arial, helvetica, sans-serif;"><span style="color: #000000;"> </span></span></div> <div style="font-size: 12.8000001907349px;"><strong>Palestrante: </strong>Enrico A. Colosimo - <span style="font-size: small;">Departamento de Estatística, UFMG (trabalho conjunto com </span><span style="font-size: small;">Jose Luiz P. da Silva e Fabio N. Demarqui</span><span style="font-size: small;">)</span></div> <div style="font-size: 12.8000001907349px;"> </div> <div style="font-size: 12.8000001907349px;"><strong>Título:</strong> <span style="font-size: small;">Doubly Robust-Based GEE for the Analysis of Longitudinal Ordinal Missing Data</span></div> <div style="font-size: 12.8000001907349px;"> </div> <div style="font-size: 12.8000001907349px;"><strong>Resumo:</strong> <span style="font-size: small;">Generalized Estimation Equations (GEE) are a well-known method for the analysis of non-Gaussian longitudinal data. This method has computational simplicity and population parameter interpretation. However, in the presence of missing data, it is only valid under the strong assumption of missing completely at random (MCAR). Some corrections can be done when the missing data mechanism is missing at random (MAR): inverse probability weighting (WGEE) and multiple imputation (MIGEE). In order to obtain consistent estimates, it is necessary the correct speci cation of the weight model for WGEE or the imputation model for the MIGEE. A recent method combining ideas of these two approaches has doubly robust property. For consistency, it requires only the weight or the imputation model to be correct. In this work it is assumed a proportional odds model and it is proposed a doubly robust estimator for the analysis of ordinal longitudinal data with intermittently missing response and covariate under the MAR mechanism. Simulation results revealed better performance of the proposed method compared to WGEE and MIGEE. The method is applied to a data set related to Analgesia Pain in Childbirth study.</span></div> <div style="font-size: 12.8000001907349px;"> </div> <div style="font-size: 12.8000001907349px;"><strong>Local: </strong>Sala de Seminários Antiga do Departamento de Estatística UFSCar</div> <div style="font-size: 12.8000001907349px;"> </div> <div style="font-size: 12.8000001907349px;"><strong>Horário: </strong>16hs<strong>.</strong></div> <div style="font-size: 12.8000001907349px;"> </div> <div style="font-size: 12.8000001907349px;"> </div> <div> <div class="gmail_signature"> <div dir="ltr"> <div>--</div> Rafael Izbicki<br />Assistant Professor<br />Department of Statistics<br />Federal University of São Carlos (UFSCar)<br /><a href="http://www.rizbicki.ufscar.br/">http://www.rizbicki.ufscar.br/</a><br /><br /></div> </div> </div> </div> <br /> <pre>_______________________________________________ abe mailing list <a href="mailto:abe@lists.ime.usp.br">abe@lists.ime.usp.br</a> <a href="https://lists.ime.usp.br/mailman/listinfo/abe">https://lists.ime.usp.br/mailman/listinfo/abe</a> </pre> </blockquote> <p> </p> <div> </div> </body></html>