TY - JOUR
T1 - Huber-type principal expectile component analysis
AU - Lin, Liang Ching
AU - Chen, Ray Bing
AU - Huang, Mong Na Lo
AU - Guo, Meihui
N1 - Funding Information:
This research was supported in part by the Mathematics Division of the National Center for Theoretical Sciences, Taiwan and the Ministry of Science and Technology in Taiwan , under the grants MOST 105-2628-M-006-001-MY3 , MOST 106-2118-M-110-003-MY2 and MOST 105-2118-M-110-002-MY2 .
Publisher Copyright:
© 2020 Elsevier B.V.
PY - 2020/11
Y1 - 2020/11
N2 - In principal component analysis (PCA), principal components are identified by maximizing the component score variance around the mean. However, a practitioner might be interested in capturing the variation in the tail rather than the center of a distribution to, for example, identify the major pollutants from air pollution data. To address this problem, we introduce a new method called Huber-type principal expectile component (HPEC) analysis that uses an asymmetric Huber norm to provide a kind of robust-tail PCA. The statistical properties of HPECs are derived, and a derivative-free optimization approach called particle swarm optimization (PSO) is used to identify HPECs numerically. As a demonstration, HPEC analysis is applied to real and simulated data with encouraging results.
AB - In principal component analysis (PCA), principal components are identified by maximizing the component score variance around the mean. However, a practitioner might be interested in capturing the variation in the tail rather than the center of a distribution to, for example, identify the major pollutants from air pollution data. To address this problem, we introduce a new method called Huber-type principal expectile component (HPEC) analysis that uses an asymmetric Huber norm to provide a kind of robust-tail PCA. The statistical properties of HPECs are derived, and a derivative-free optimization approach called particle swarm optimization (PSO) is used to identify HPECs numerically. As a demonstration, HPEC analysis is applied to real and simulated data with encouraging results.
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U2 - 10.1016/j.csda.2020.106992
DO - 10.1016/j.csda.2020.106992
M3 - Article
AN - SCOPUS:85084943331
SN - 0167-9473
VL - 151
JO - Computational Statistics and Data Analysis
JF - Computational Statistics and Data Analysis
M1 - 106992
ER -