[Forum SIS] avviso di seminario - N. Salvati, Università di Pisa

[Maria Giovanna Ranalli]

.: Nicola Salvati, Dipartimento di Statistica e Matematica Applicata  
all'Economia, Università di Pisa :.

Venerdi 22 Ottobre, ore 12, Aula 201
Università degli Studi di Perugia, Dipartimento di Economia, Finanza e  
Statistica

.: M-quantile and Expectile Random Effects Regression for Multilevel  
Data :.

Abstract:
The analysis of hierarchically structured data, for example  
longitudinal data or geographically clustered data, is usually carried  
out by using random effects models. The primary goal of random effects  
regression is to model the expected value of the conditional  
distribution of an outcome variable given a set of explanatory  
variables while simultaneously accounting for the dependence structure  
of hierarchical data. The expected value, however, does not always  
offer a complete picture of this conditional distribution. In this  
paper we show how one can overcome this problem by fitting  
hierarchical linear models to the M-quantiles of the conditional  
distribution of the outcome variable given the covariates. Our  
proposed M-quantile random effects regression model extends M-  
quantile regression (Breckling and Chambers, 1988) and can be  
considered as an alternative to the quantile random effects model  
(Geraci and Bottai, 2007). M-estimation is synonymous with outlier-  
robust estimation, and so robust estimation of both fixed and random  
effects is straightforward. An advantage of the M-estimation framework  
is that it also allows for the use of expectile regression. This can  
potentially lead to efficiency gains when the use of outlier-robust  
estimation methods is not justified but there is still interest in  
modelling the conditional distribution of the variable of interest. We  
show how a modified maximum likelihood approach can be used to fit the  
M-quantile random effects regression model, and inference for  
estimators of the fixed and random effects parameters is discussed.  
The performance of this approach is then evaluated in a series of  
simulation studies. We conclude the paper by describing a case study  
where both M-quantile and expectile random effects regression are used  
to analyse repeated measures data collected from a rotary pursuit  
tracking experiment.


tutte le informazioni su http://www.ec.unipg.it/DEFS/

cordiali saluti

M. Giovanna Ranalli

~ Dipartimento di Economia, Finanza e Statistica
~ Sezione di Statistica
~ Via Pascoli
~ Universita' degli Studi di Perugia
~ 06123 Perugia - Italy

~ Tel +39 075 5855939
~ Fax +39 075 5855950
~ url: www.stat.unipg.it/~giovanna