[Statlist] Séminaires de statistique jeudi 8 mars 12h et vendredi 9 mars 11h

[ISTAT Messagerie]

SEMINAIRE DE STATISTIQUE

Institut de Statistique, Université de Neuchâtel, Pierre-à-Mazel 7, 2000 Neuchâtel- http://www2.unine.ch/statistics   

JEUDI 8 mars 2012 12h00, salle PAM 110, 1er étage

David Haziza 
University of Montreal

Controling the bias of robust small area predictors 
Joint work with Valéry Dongmo Jiongo (Statistics Canada) and Pierre Duchesne (University of Montreal)
The user demand for small area estimators has been growing in most countries. This led survey statisticians to develop theoretically sound and yet practical estimation procedures, providing reliable estimators for small areas. A popular estimation method is the so-called empirical best linear unbiased prediction (EBLUP). However, the EBLUP is sensitive to the presence of outliers. That is, including or excluding outlying units from its computation may have a large impact on its magnitude. In recent years, the problem of robust small area estimation has received considerable interest. Sinha and Rao (2009) proposed estimation procedures designed for small areas, which are based on empirical best linear unbiased prediction estimators, properly modified in order to be robust to outliers. Also, bias-corrected estimators have been proposed by Chambers, Chandra, Salvati and Tzavidis (2009). In this talk, we introduce two new robust small area estimators that are robust to the presence of outliers. The first estimator is motivated by a decomposition similar in spirit to that of Chambers (1986) in the context of fixed effect models. Following Beaumont, Haziza and Ruiz-Gazen (2011), the second estimator is constructed using the estimated conditional bias of a unit, which can be interpreted as a measure of influence. The proposed robust estimators involve a psi-function, which depends on a tuning constant. The choice of this constant will be discussed. Finally, we will present the results of a simulation study that compares the performance of several robust estimators in terms of relative bias and relative efficiency.


VENDREDI 9 mars 2012 11h00, salle PAM 110, 1er étage

David Ardia
Tolomeo Capital AG

Fully Flexible Views in Multivariate Normal Markets
The Entropy Pooling approach in Meucci (2008) is a versatile, general framework to process market views in portfolio construction and generalized stress-tests in risk management. Here we present an efficient algorithm to implement Entropy Pooling with fully general views in multivariate normal markets. Then we discuss two applications. First, we use normal Entropy Pooling to estimate a market distribution consistent with the CAPM equilibrium. Second, we use normal Entropy Pooling to process ranking signals for alpha-generation.