[Ml-stat-talks] [stat seminar reminder]: Friday 2:30pm by Prof. David Tyler

xtong at princeton.edu xtong at princeton.edu
Thu Mar 3 21:58:23 EST 2011

 Hi All,

 This is a reminder that we will have Prof. David Tyler from Rutgers
University speak in the stat lab at 2:30pm tomorrow on Friday.  

 Topic:  Robust functional principal components: A projection-pursuit


 In many situations, data are not simply univariate or multivariate
 observations, but rather a data point may be a function itself, e.g.
 data which is recorded over a period of time. The statistical study of
 such data is commonly referred to as functional data analysis.

 Functional data analysis methods are typically extensions of multivariate
 methods, such as principal components analysis. As with multivariate
 it is possible to have hidden outliers in functional data and so there is
 a need for robust methods in this area.  The literature on robust
 and in particular robust principal components in the functional data
 setting though is rather sparse.

 In this talk, the difficulty of extending robust multivariate principal
 components analysis to the functional data setting is first discussed.
 It is then noted that one promising robust method which can be extended
 the projection pursuit approach to robust principal component analysis.
 This approach for functional data, together with different smoothing
 methods, is presented and studied. Consistency results are shown under
 mild assumptions. The performance of the classical and robust procedures
 are compared via a simulation study under different contamination

 This work is joint with Luca Bali and Graciela Boente of the University
 of Buenos Aires and with Jane-Ling Wang of UCDavis.  
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