Statistical Learning with Sparsity

Focusing on the challenges posed by big data, this book explores how the sparsity assumption can help extract meaningful patterns from extensive datasets, even when the number of features exceeds observations. It delves into various techniques, including the lasso for linear regression, generalized penalties, and numerical optimization methods. Additionally, it covers statistical inference for lasso models, sparse multivariate analysis, graphical models, and compressed sensing, providing a comprehensive guide to modern data analysis techniques.