Abstract
The goal of this paper is to demonstrate that established rank correlation
measures are not ideally suited for measuring rank correlation for numerical
data that are perturbed by noise. We propose to use robust rank correlation
measures based on fuzzy orderings. We demonstrate that the new measures
overcome the robustness problems of existing rank correlation coefficients. As
a first step, this is accomplished by illustrative examples. The paper closes
with an outlook on future research and applications.
| Original language | English |
|---|---|
| Pages (from-to) | 5-20 |
| Number of pages | 16 |
| Journal | Mathware and Soft Computing |
| Volume | 15 |
| Issue number | 2 |
| Publication status | Published - 2008 |
Fields of science
- 101004 Biomathematics
- 101027 Dynamical systems
- 101028 Mathematical modelling
- 101029 Mathematical statistics
- 101014 Numerical mathematics
- 101015 Operations research
- 101016 Optimisation
- 101017 Game theory
- 101018 Statistics
- 101019 Stochastics
- 101024 Probability theory
- 101026 Time series analysis
- 102 Computer Sciences
- 102001 Artificial intelligence
- 102003 Image processing
- 102004 Bioinformatics
- 102013 Human-computer interaction
- 102018 Artificial neural networks
- 102019 Machine learning
- 103029 Statistical physics
- 106005 Bioinformatics
- 106007 Biostatistics
- 202017 Embedded systems
- 202035 Robotics
- 202036 Sensor systems
- 202037 Signal processing
- 305901 Computer-aided diagnosis and therapy
- 305905 Medical informatics
- 305907 Medical statistics
- 102032 Computational intelligence
- 102033 Data mining
- 101031 Approximation theory
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