Confidence Region

Confidence regions aim to quantify the uncertainty associated with estimated parameters or predictions in statistical models, providing a range of plausible values rather than a single point estimate. Current research focuses on developing robust methods for constructing these regions, particularly for high-dimensional data and complex models, employing techniques like debiased LASSO, neural conditional probability models, and conformal prediction. These advancements are crucial for reliable inference in diverse fields, improving the accuracy and trustworthiness of scientific findings and informing decision-making in applications ranging from medical imaging to robust optimization.