Yuping Wang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Yuping Wang has a total of 1 co-authors. Here are the top co-authors by number of shared publications:
- Sebastian Fuchs
University of Salzburg Austria
2 shared publications
Latest Publications
- 10 August 2024
- Published by Springer Nature
- p. 548-553
Hierarchical Variable Clustering Based on Measures of Predictability
- 10 August 2024
- Published by Springer Nature
- p. 548-553
Hierarchical Variable Clustering Based on Measures of Predictability
- Open Access
Hierarchical variable clustering based on the predictive strength between random vectors
A rank-invariant clustering of variables is introduced that is based on the predictive strength between groups of variables, i.e., two groups are assigned a high similarity if the variables in the first group contain high predictive information about the behaviour of the variables in the other group and/or vice versa. The method presented here is model-free, dependence-based and does not require any distributional assumptions. Various general invariance and continuity properties are investigated, with special attention to those that are beneficial for the agglomerative hierarchical clustering procedure. A fully non-parametric estimator is considered whose excellent performance is demonstrated in several simulation studies and by means of real-data examples.
Open AccessHierarchical variable clustering based on the predictive strength between random vectors
A rank-invariant clustering of variables is introduced that is based on the predictive strength between groups of variables, i.e., two groups are assigned a high similarity if the variables in the first group contain high predictive information about the behaviour of the variables in the other group and/or vice versa. The method presented here is model-free, dependence-based and does not require any distributional assumptions. Various general invariance and continuity properties are investigated, with special attention to those that are beneficial for the agglomerative hierarchical clustering procedure. A fully non-parametric estimator is considered whose excellent performance is demonstrated in several simulation studies and by means of real-data examples.