[Statlist] Séminaires de Statistique, Institut de Statistique, Université de Neuchâtel

KONDYLIS Atanassios atanassios.kondylis at unine.ch
Tue Nov 21 09:36:20 CET 2006


Séminaires de Statistique 
Institut de Statistique, Université de Neuchâtel 
Pierre à Mazel 7 (1er étage,salle 101), Neuchâtel,
http://www2.unine.ch/statistics


Mardi 28 novembre  2006 à 11h00 
Eva Cantoni, Université de Genève, Switzerland

Title: Variable Selection in marginal Longitudinal Models

Abstract: Variable selection is an important step in any statistical analysis. If this issue is well 
adressed in the linear regression setting for example, it was not the case until recently for marginal 
longitudinal models. I will present a generalized version of Mallows's Cp to be used for variables 
selection in the setting of marginal longitudinal models. The definition of this criterion is very general 
so that it can also address robustness, heteroscedasticity, and missing values. I will go on to present 
a Monte Carlo Markov Chain technique that allows to handle situations where the number of 
covariates is very large and where therefore a criterion that has to be computed for each model cannot 
be considered.

References: 
E. Cantoni,  J. Mills Flemming & E. Ronchetti (2005). "Variable Selection for Marginal Longitudinal Generalized 
Linear Models",  Biometrics, 61, 507-514.  
E. Cantoni,  C. Field ,  J. Mills Flemming & E. Ronchetti (2006). "Longitudinal variable selection by cross-validation 
in the case of many covariates", to appear in Statistics in Medicine (DOI 10.1002/sim.2572)


-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
Séminaires de Statistique 
Institut de Statistique, Université de Neuchâtel 
Pierre à Mazel 7 (1er étage,salle 101), Neuchâtel,
http://www2.unine.ch/statistics


Mardi 19 december 2006 à 11h00 
Ingrid Van Keilegom, Institut de statistique, UCL, Louvain-la-Neuve, Belgium 

Title : A Goodness-of-fit Test for Semiparametric Models in Multiresponse Regression

Abstract : We propose an empirical likelihood test that is able to test the goodness-of-fit of a class of 
semiparametric regression models. The class includes as special cases fully parametric models, semiparametric 
models, like the multi-index and the partially linear models, and models with shape constraints, like monotone 
regression models. Another feature of the test is that it allows both the response variable and the covariate be 
multivariate which means that multiple regression curves can be tested simultaneously. The test also allows the 
presence of infinite dimensional nuisance parameters in the model to be tested. It is shown that the empirical likelihood 
test statistic is asymptotically normally distributed under certain mild conditions and permits a wild bootstrap calibration. 
Despite the fact that the class of models which can be detected consistently by the proposed test is very large, the 
empirical likelihood test enjoys good power properties against departures from a hypothesized model within the class. 

This is joint work with Song Chen, Iowa State University.



More information about the Statlist mailing list