Abstract
This article proposes a simulation-based method to adjust functional boxplots for correlations when visualizing functional and spatio-temporal data, as well as detecting outliers. We start by investigating the relationship between the spatio-temporal dependence and the 1.5 times the 50% central region empirical outlier detection rule. Then, we propose to simulate observations without outliers on the basis of a robust estimator of the covariance function of the data. We select the constant factor in the functional boxplot to control the probability of correctly detecting no outliers. Finally, we apply the selected factor to the functional boxplot of the original data. As applications, the factor selection procedure and the adjusted functional boxplots are demonstrated on sea surface temperatures, spatio-temporal precipitation and general circulation model (GCM) data. The outlier detection performance is also compared before and after the factor adjustment.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 54-64 |
| Number of pages | 11 |
| Journal | Environmetrics |
| Volume | 23 |
| Issue number | 1 |
| DOIs | |
| State | Published - Feb 2012 |
| Externally published | Yes |
Bibliographical note
KAUST Repository Item: Exported on 2020-10-01Acknowledged KAUST grant number(s): KUS-C1-016-04
Acknowledgements: This research was partially supported by NSF grants DMS-1007504, DMS-1100492, and Award No. KUS-C1-016-04, made by King Abdullah University of Science and Technology (KAUST). The authors thank the Guest Editor, two referees and Noel Cressie for helpful comments, as well as Caspar M. Ammann for providing the GCM data.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.
Keywords
- Functional data
- GCM data
- Outlier detection
- Precipitation data
- Robust covariance
- Spatio-temporal data
ASJC Scopus subject areas
- Ecological Modeling
- Statistics and Probability