التفاصيل البيبلوغرافية
| العنوان: |
S -Estimators for Functional Principal Component Analysis. |
| المؤلفون: |
Boente, Graciela (AUTHOR) gboente@dm.uba.ar, Salibian-Barrera, Matías (AUTHOR) gboente@dm.uba.ar |
| المصدر: |
Journal of the American Statistical Association. Sep2015, Vol. 110 Issue 511, p1100-1111. 12p. |
| مصطلحات موضوعية: |
*ESTIMATION theory, *APPROXIMATION theory, *MULTIVARIATE analysis, INFINITY (Mathematics), HILBERT space |
| مستخلص: |
Principal component analysis is a widely used technique that provides an optimal lower-dimensional approximation to multivariate or functional datasets. These approximations can be very useful in identifying potential outliers among high-dimensional or functional observations. In this article, we propose a new class of estimators for principal components based on robust scale estimators. For a fixed dimensionq, we robustly estimate theq-dimensional linear space that provides the best prediction for the data, in the sense of minimizing the sum of robust scale estimators of the coordinates of the residuals. We also study an extension to the infinite-dimensional case. Our method is consistent for elliptical random vectors, and is Fisher consistent for elliptically distributed random elements on arbitrary Hilbert spaces. Numerical experiments show that our proposal is highly competitive when compared with other methods. We illustrate our approach on a real dataset, where the robust estimator discovers atypical observations that would have been missed otherwise. Supplementary materials for this article are available online. [ABSTRACT FROM PUBLISHER] |
|
Copyright of Journal of the American Statistical Association is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| قاعدة البيانات: |
Business Source Index |
