A distribution-free valid p-value for finite samples of bounded random variables
<|reference_start|>A distribution-free valid p-value for finite samples of bounded random variables: We build a valid p-value based on a concentration inequality for bounded random variables introduced by Pelekis, Ramon and Wang. The motivation behind this work is the calibration of predictive algorithms in a distribution-free setting. The super-uniform p-value is tighter than Hoeffding and Bentkus alternatives in certain regions. Even though we are motivated by a calibration setting in a machine learning context, the ideas presented in this work are also relevant in classical statistical inference. Furthermore, we compare the power of a collection of valid p- values for bounded losses, which are presented in previous literature.<|reference_end|>
@article{alvarez2024a,
    title={A distribution-free valid p-value for finite samples of bounded random
  variables},
    author={Joaquin Alvarez},
    journal={arXiv preprint arXiv:2405.08975},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.08975},
    primaryClass={stat.ML cs.LG math.ST stat.TH}
}