Huber-type principal expectile component analysis

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

研究成果: Article同行評審

2 引文 斯高帕斯(Scopus)


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.

期刊Computational Statistics and Data Analysis
出版狀態Published - 2020 11月

All Science Journal Classification (ASJC) codes

  • 統計與概率
  • 計算數學
  • 計算機理論與數學
  • 應用數學


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