Interval Estimation

Interval estimation focuses on providing a range of plausible values for a parameter of interest, rather than a single point estimate, thus quantifying uncertainty. Current research emphasizes developing robust methods for various applications, including machine learning model evaluation, voltage prediction in microchips, and forensic analysis, employing techniques like conformal prediction, quantile regression, and bootstrapping alongside neural networks. This improved uncertainty quantification enhances the reliability and trustworthiness of predictions across diverse scientific fields and practical applications, leading to more informed decision-making. The development of distribution-free methods and those addressing high-dimensional data are particularly active areas.