Hypothesis Testing

Hypothesis testing, a cornerstone of statistical inference, aims to determine whether observed data supports a particular hypothesis over alternatives. Current research emphasizes robust methods for handling uncertainty, including non-parametric approaches and techniques that incorporate distributional uncertainty or misclassification penalties, often within the context of machine learning models like deep neural networks and ensemble methods. These advancements are crucial for addressing challenges in diverse fields, from ensuring privacy in data analysis to improving the reliability and interpretability of scientific findings and machine learning model outputs. The development of efficient and accurate hypothesis testing methods under various constraints, such as limited data or computational resources, remains a key focus.

Papers