Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/148876 
Erscheinungsjahr: 
2016
Schriftenreihe/Nr.: 
SFB 649 Discussion Paper No. 2016-040
Verlag: 
Humboldt University of Berlin, Collaborative Research Center 649 - Economic Risk, Berlin
Zusammenfassung: 
Principal component analysis (PCA) is a widely used dimension reduction tool in the analysis of high-dimensional data. However, in many applications such as risk quantification in finance or climatology, one is interested in capturing the tail variations rather than variation around the mean. In this paper, we develop Principal Expectile Analysis (PEC), which generalizes PCA for expectiles. It can be seen as a dimension reduction tool for extreme value theory, where one approximates uctuations in the expectile level of the data by a low dimensional subspace. We provide algorithms based on iterative least squares, prove upper bounds on their convergence times, and compare their performances in a simulation study. We apply the algorithms to a Chinese weather dataset and fMRI data from an investment decision study.
Schlagwörter: 
principal components
asymmetric norm
dimension reduction
quantile
expectile
fMRI
risk attitude
brain imaging
temperature
functional data
JEL: 
C38
C55
C61
C63
D81
Dokumentart: 
Working Paper

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