Gaussian Design

Gaussian design, a statistical model assuming data points are drawn from a Gaussian distribution, is central to analyzing high-dimensional data problems like sparse linear regression and principal component analysis (PCA). Current research focuses on extending its applicability beyond idealized scenarios, addressing challenges posed by real-world data exhibiting correlations, outliers, and non-Gaussian characteristics through techniques such as approximate message passing and robust loss functions. These advancements are crucial for improving the accuracy and reliability of statistical methods in diverse fields, including medical imaging, genetics, and machine learning, where the assumptions of classical Gaussian design often fail.