Statistical Inference

Statistical inference aims to draw reliable conclusions and quantify uncertainty about model parameters or hypotheses based on observed data. Current research emphasizes robust methods for high-dimensional data, addressing challenges like data scarcity, bias (including sampling bias), and the need for distributed computation, often employing techniques like federated learning, bootstrap methods, and various regularization approaches (e.g., Lasso). These advancements are crucial for ensuring the validity and reliability of inferences across diverse fields, from machine learning and causal inference to anomaly detection and scientific discovery, improving decision-making in high-stakes applications.