| Autor: |
Pokotylo, Oleksii, Mozharovskyi, Pavlo, Dyckerhoff, Rainer |
| Rok vydání: |
2016 |
| Předmět: |
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| Druh dokumentu: |
Working Paper |
| Popis: |
Following the seminal idea of Tukey, data depth is a function that measures how close an arbitrary point of the space is located to an implicitly defined center of a data cloud. Having undergone theoretical and computational developments, it is now employed in numerous applications with classification being the most popular one. The R-package ddalpha is a software directed to fuse experience of the applicant with recent achievements in the area of data depth and depth-based classification. ddalpha provides an implementation for exact and approximate computation of most reasonable and widely applied notions of data depth. These can be further used in the depth-based multivariate and functional classifiers implemented in the package, where the $DD\alpha$-procedure is in the main focus. The package is expandable with user-defined custom depth methods and separators. The implemented functions for depth visualization and the built-in benchmark procedures may also serve to provide insights into the geometry of the data and the quality of pattern recognition. |
| Databáze: |
arXiv |
| Externí odkaz: |
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