[ABE-L] Fwd: Workshop on Longitudinal and Incomplete Data, 24 a 28/11/2014, por Geert Molenberghs (university of Hasselt, Belgium)

[clarice.demetrio em usp.br]

Título: Workshop on Longitudinal and Incomplete Data 
Palestrante: Geert Molenberghs (University of Hasselt, Belgium) 
Local: Depto de Ciências Exatas, ESALQ/USP, Piracicaba, SP 
Período: 24 a 28/11/2014 (9 as 12 e 14 as 17h) 

Inscrições: http://fealq.org.br/informacoes-do-evento/?id=209 

> Abstract
> First present linear mixed models for continuous hierarchical data.
> The focus lies on the modeler’s perspective and on applications.
> Emphasis will be on model formulation, parameter estimation, and
> hypothesis testing, as well as on the distinction between the
> random-effects (hierarchical) model and the implied marginal model.
> Apart from classical model building strategies, many of which have
> been implemented in standard statistical software, a number of
> flexible extensions and additional tools for model diagnosis will be
> indicated. Second, models for non-Gaussian data will be discussed,
> with a strong emphasis on generalized estimating equations (GEE) and
> the generalized linear mixed model (GLMM). To usefully introduce
> this theme, a brief review of the classical generalized linear
> modeling framework will be presented. Similarities and differences
> with the continuous case will be discussed. The differences between
> marginal models, such as GEE, and random-effects models, such as the
> GLMM, will be explained in detail. Third, when analysing
> hierarchical and longitudinal data, one is often confronted with
> missing observations, i.e., scheduled measurements have not been
> made, due to a variety of (known or unknown) reasons. It will be
> shown that, if no appropriate measures are taken, missing data can
> cause seriously jeopardize results, and interpretational
> difficulties are bound to occur. Methods to properly analyze
> incomplete data, under flexible assumptions, are presented. Key
> concepts of sensitivity analysis are introduced. Throughout the
> workshop, it will be assumed that the participants are familiar with
> basic statistical modelling, including linear models (regression and
> analysis of variance), as well as generalized linear models
> (logistic and Poisson regression). Moreover, pre-requisite knowledge
> should also include general estimation and testing theory (maximum
> likelihood, likelihood ratio). All developments will be illustrated
> with worked examples using the SAS System. These will be
> supplemented with practical sessions.

>  
-------------- Próxima Parte ----------
Um anexo em HTML foi limpo...
URL: <https://lists.ime.usp.br/archives/abe/attachments/20140926/14df0353/attachment.html>