Multivariate factorizable expectile regression with application to fMRI data
Journal
Computational Statistics & Data Analysis
ISSN
0167-9473
ISSN-Digital
1872-7352
Type
journal article
Date Issued
2018-05
Author(s)
Abstract
A multivariate expectile regression model is proposed to analyze the tail events of large cross-sectional and spatial data, where the tail events are linked by a latent factor structure. The computational advantage of the method is demonstrated, and the estimation risk is analyzed for every fixed number of iteration and fixed sample size, when the latent factors are either exactly or approximately sparse. The proposed method is applied on the functional magnetic resonance imaging (fMRI) data taken during an experiment of investment decisions making. It is shown that the negative extreme blood oxygenation level dependent (BOLD) responses may be relevant to the risk preferences.
Language
English
HSG Profile Area
SEPS - Quantitative Economic Methods
Refereed
Yes
Publisher
Elsevier Science
Publisher place
Amsterdam
Volume
121
Start page
1
End page
19
Pages
19
Official URL
Subject(s)
Division(s)
Eprints ID
253372