Multivariate factorizable expectile regression with application to fMRI data

Item Type Journal paper
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.

Authors Chao, Shih-Kang; Härdle, Wolfgang & Huang, Chen
Journal or Publication Title Computational Statistics and Data Analysis
Language English
Subjects other research area
HSG Profile Area SEPS - Quantitative Economic Methods
Refereed Yes
Date 2018
Publisher Elsevier Science
Volume 121
Page Range 1-19
Number of Pages 19
ISSN 0167-9473
Publisher DOI 10.1016/j.csda.2017.12.001
Official URL https://doi.org/10.1016/j.csda.2017.12.001
Depositing User Dr. Chen Huang
Date Deposited 18 Jan 2018 14:45
Last Modified 18 Jan 2018 14:47
URI: https://www.alexandria.unisg.ch/publications/253372

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Citation

Chao, Shih-Kang; Härdle, Wolfgang & Huang, Chen (2018) Multivariate factorizable expectile regression with application to fMRI data. Computational Statistics and Data Analysis, 121 1-19. ISSN 0167-9473

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https://www.alexandria.unisg.ch/id/eprint/253372
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