2009
NIPS
NeurIPS 2009
Efficient Moments-based Permutation Tests
Abstract
In this paper, we develop an efficient moments-based permutation test approach to improve the system’s efficiency by approximating the permutation distribution of the test statistic with Pearson distribution series. This approach involves the calculation of the first four moments of the permutation distribution. We propose a novel recursive method to derive these moments theoretically and analytically without any permutation. Experimental results using different test statistics are demonstrated using simulated data and real data. The proposed strategy takes advantage of nonparametric permutation tests and parametric Pearson distribution approximation to achieve both accuracy and efficiency.
🌉
Interdisciplinary Bridge
— Machine Learning and Mathematics & Optimization
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Keyword Pioneer
— pearson distribution
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Cross-Pollinator
— Artificial Intelligence, Computer Vision, Data Science & Analytics, Deep Learning, Interdisciplinary, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Security & Privacy
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Trend Setter
— Statistics
Authors
Topics
Machine Learning > Optimization & Theory > Statistical Learning
Mathematics & Optimization > Mathematics > Statistics
Mathematics & Optimization > Statistics
Machine Learning > Optimization & Theory > Statistics
Mathematics & Optimization > Statistics > Statistics
Machine Learning > Core Methods > Evaluation
Machine Learning > Learning Types > Statistical Learning