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Bagging Nearest-Neighbor Prediction independence Test: an efficient method for nonlinear dependence of two continuous
Yi Wang1, Yi Li1, Xiaoyu Liu1
1Ministry of Education Key Laboratory of Contemporary Anthropology, Collaborative Innovation Center for Genetics and Development, School of Life Sciences, Fudan University, Shanghai, China.
This study introduces Bagging Nearest-Neighbor Prediction independence Test (BNNPT) for efficient nonlinear dependence testing between continuous variables. BNNPT demonstrates strong performance in simulations and real-world data analysis.
Area of Science:
- Statistics
- Computational Biology
- Bioinformatics
Background:
- Testing dependence between variables is crucial in statistical analysis.
- Existing methods may not efficiently capture nonlinear relationships.
Purpose of the Study:
- To propose an efficient method for testing nonlinear dependence between two continuous variables.
- To introduce the Bagging Nearest-Neighbor Prediction independence Test (BNNPT) framework.
Main Methods:
- Utilized X to build a bagging neighborhood structure.
- Obtained out-of-bag estimators of Y based on the neighborhood structure.
- Applied permutation testing to assess the significance of prediction errors.
Main Results:
- BNNPT demonstrated efficiency in predicting Y from X.
- Evaluated BNNPT against seven other methods using simulations and real datasets.
- Compared false positive rates and statistical power across methods.
Conclusions:
- BNNPT is an efficient computational approach for testing nonlinear correlation.
- The method shows promise for real-world applications in diverse datasets.
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