Huber-type principal expectile component analysis

Liang-Ching Lin, Ray-Bing Chen, Mong-Na Lo Huang and Meihui Guo

Computational Statistics & Data Analysis, 2020, vol. 151, issue C

Abstract: 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.

Keywords: Asymmetric norm; Expectile; Huber’s criterion; Particle swarm optimization; Principal component (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:151:y:2020:i:c:s0167947320300839

DOI: 10.1016/j.csda.2020.106992

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