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- Title
On Robustness for Spatio-Temporal Data.
- Authors
García-Pérez, Alfonso
- Abstract
The spatio-temporal variogram is an important factor in spatio-temporal prediction through kriging, especially in fields such as environmental sustainability or climate change, where spatio-temporal data analysis is based on this concept. However, the traditional spatio-temporal variogram estimator, which is commonly employed for these purposes, is extremely sensitive to outliers. We approach this problem in two ways in the paper. First, new robust spatio-temporal variogram estimators are introduced, which are defined as M-estimators of an original data transformation. Second, we compare the classical estimate against a robust one, identifying spatio-temporal outliers in this way. To accomplish this, we use a multivariate scale-contaminated normal model to produce reliable approximations for the sample distribution of these new estimators. In addition, we define and study a new class of M-estimators in this paper, including real-world applications, in order to determine whether there are any significant differences in the spatio-temporal variogram between two temporal lags and, if so, whether we can reduce the number of lags considered in the spatio-temporal analysis.
- Subjects
VARIOGRAMS; SADDLEPOINT approximations; SUSTAINABILITY; CLIMATE change; ROBUST statistics; DATA analysis
- Publication
Mathematics (2227-7390), 2022, Vol 10, Issue 10, p1785
- ISSN
2227-7390
- Publication type
Article
- DOI
10.3390/math10101785