Summary of A Distribution-free Valid P-value For Finite Samples Of Bounded Random Variables, by Joaquin Alvarez
A distribution-free valid p-value for finite samples of bounded random variables
by Joaquin Alvarez
First submitted to arxiv on: 14 May 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Statistics Theory (math.ST)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed approach builds upon a concentration inequality for bounded random variables to derive a valid p-value. This effort aims to calibrate predictive algorithms in a distribution-free setting, leveraging insights from machine learning and classical statistical inference. The resulting super-uniform p-value is shown to be tighter than existing alternatives in certain regions. Additionally, the study compares the power of various valid p-values for bounded losses, drawing upon previous literature. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research creates a new way to calculate a p-value that’s based on how likely it is for something to happen by chance. This helps us understand and improve machine learning models so they make better predictions. The method works well in situations where we don’t know the underlying distribution of the data, which is important because real-world data can be complex. The findings also apply to traditional statistics, making this a valuable contribution. |
Keywords
» Artificial intelligence » Inference » Machine learning
