Estimation and Testing Under Sparsity: École d'Été de Probabilités de Saint-Flour XLV - 2015

By Sara Van de Geer

Taking the Lasso method as its starting point, this book describes the main ingredients needed to study general loss functions and sparsity-inducing regularizers. It also provides a semi-parametric approach to establishing confidence intervals and tests. Sparsity-inducing methods have proven to be very useful in the analysis of high-dimensional data. Examples include the Lasso and group Lasso methods, and the least squares method with other norm-penalties, such as the nuclear norm. The illustrations provided include generalized linear models, density estimation, matrix completion and sparse principal components. Each chapter ends with a problem section. The book can be used as a textbook for a graduate or PhD course.

• Format: Paperback
• Language: English
• ISBN: 3319327739
• ISBN13: 9783319327730
• Release Date: June 2016
• Publisher: Springer
• Length: 274 Pages
• Weight: 0.91 lbs.
• Dimensions: 0.6" x 6.1" x 9.2"