Abstract
We propose new tools for visualizing large amounts of functional data in the form of smooth curves. The proposed tools include functional versions of the bagplot and boxplot, which make use of the first two robust principal component scores, Tukey’s data depth and highest density regions.
By-products of our graphical displays are outlier detection methods for functional data. We compare these new outlier detection methods with existing methods for detecting outliers in functional data, and show that our methods are better able to identify outliers.
An R-package containing computer code and datasets is available in the online supplements.
By-products of our graphical displays are outlier detection methods for functional data. We compare these new outlier detection methods with existing methods for detecting outliers in functional data, and show that our methods are better able to identify outliers.
An R-package containing computer code and datasets is available in the online supplements.
| Original language | English |
|---|---|
| Pages (from-to) | 29-45 |
| Number of pages | 17 |
| Journal | Journal of Computational and Graphical Statistics |
| Volume | 19 |
| Issue number | 1 |
| DOIs | |
| Publication status | Published - 2010 |
| Externally published | Yes |
Keywords
- Highest density regions
- Kernel density estimation
- Outlier detection
- Robust principal component analysis
- Tukey’s halfspace location depth