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Research Paper#Statistics, Multiple Testing, Empirical Bayes🔬 ResearchAnalyzed: Jan 3, 2026 08:54

Improving Power in One-Sided Multiple Testing

Published:Dec 31, 2025 03:26
1 min read
ArXiv

Analysis

This paper addresses the problem of conservative p-values in one-sided multiple testing, which leads to a loss of power. The authors propose a method to refine p-values by estimating the null distribution, allowing for improved power without modifying existing multiple testing procedures. This is a practical improvement for researchers using standard multiple testing methods.
Reference

“The proposed method substantially improves power when p-values are conservative, while achieving comparable performance to existing methods when p-values are exact.”

PermalinkArXiv
Research Paper#Statistical Process Control, Conformal Prediction, Anomaly Detection🔬 ResearchAnalyzed: Jan 3, 2026 18:36

Distribution-Free Process Monitoring with Conformal Prediction

Published:Dec 29, 2025 16:56
1 min read
ArXiv

Analysis

This paper addresses a key limitation of traditional Statistical Process Control (SPC) – its reliance on statistical assumptions that are often violated in complex manufacturing environments. By integrating Conformal Prediction, the authors propose a more robust and statistically rigorous approach to quality control. The novelty lies in the application of Conformal Prediction to enhance SPC, offering both visualization of process uncertainty and a reframing of multivariate control as anomaly detection. This is significant because it promises to improve the reliability of process monitoring in real-world scenarios.
Reference

“The paper introduces 'Conformal-Enhanced Control Charts' and 'Conformal-Enhanced Process Monitoring' as novel applications.”

PermalinkArXiv
Research#llm📝 BlogAnalyzed: Dec 27, 2025 17:32

Validating Validation Sets

Published:Dec 27, 2025 16:16
1 min read
r/MachineLearning

Analysis

This article discusses a method for validating validation sets, particularly when dealing with small sample sizes. The core idea involves resampling different holdout choices multiple times to create a histogram, allowing users to assess the quality and representativeness of their chosen validation split. This approach aims to address concerns about whether the validation set is effectively flagging overfitting or if it's too perfect, potentially leading to misleading results. The provided GitHub link offers a toy example using MNIST, suggesting the principle's potential for broader application pending rigorous review. This is a valuable exploration for improving the reliability of model evaluation, especially in data-scarce scenarios.
Reference

“This exploratory, p-value-adjacent approach to validating the data universe (train and hold out split) resamples different holdout choices many times to create a histogram to shows where your split lies.”

Permalinkr/MachineLearning