https://scholars.lib.ntu.edu.tw/handle/123456789/629193
標題: | Robust self-tuning semiparametric PCA for contaminated elliptical distribution | 作者: | HUNG HUNG Su-Yun Huang Shinto Eguchi |
關鍵字: | Active ratio; elliptical distributions; influence function; PCA; robustness; semiparametric theory; Tyler's M-estimator; MULTIVARIATE LOCATION; M-ESTIMATORS; COMPONENT ANALYSIS; OUTLIER DETECTION; R-ESTIMATION; PRINCIPAL; SCATTER; COVARIANCE; SHAPE; REGRESSION; Statistics - Methodology; Statistics - Methodology | 公開日期: | 8-六月-2022 | 出版社: | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | 卷: | 70 | 起(迄)頁: | 5885 | 來源出版物: | IEEE Transactions on Signal Processing | 摘要: | Principal component analysis (PCA) is one of the most popular dimension reduction methods. The usual PCA is known to be sensitive to the presence of outliers, and thus many robust PCA methods have been developed. Among them, the Tyler's M-estimator is shown to be the most robust scatter estimator under the elliptical distribution. However, when the underlying distribution is contaminated and deviates from ellipticity, Tyler's M-estimator might not work well. In this article, we apply the semiparametric theory to propose a robust semiparametric PCA. The merits of our proposal are twofold. First, it is robust to heavy-tailed elliptical distributions as well as robust to non-elliptical outliers. Second, it pairs well with a data-driven tuning procedure, which is based on active ratio and can adapt to different degrees of data outlyingness. Theoretical properties are derived, including the influence functions for various statistical functionals and asymptotic normality. Simulation studies and a data analysis demonstrate the superiority of our method. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/629193 | ISSN: | 1053-587X | DOI: | 10.1109/TSP.2022.3230336 |
顯示於: | 流行病學與預防醫學研究所 |
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