Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/148876
Authors: 
Tran, Ngoc Mai
Burdejová, Petra
Osipenko, Maria
Härdle, Wolfgang
Year of Publication: 
2016
Series/Report no.: 
SFB 649 Discussion Paper 2016-040
Abstract: 
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.
Subjects: 
principal components
asymmetric norm
dimension reduction
quantile
expectile
fMRI
risk attitude
brain imaging
temperature
functional data
JEL: 
C38
C55
C61
C63
D81
Document Type: 
Working Paper

Files in This Item:
File
Size





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.