Lecture Notes in Mathematics: École d'Été de Probabilités de Saint-Flour XLV – 2015

van de Geer, Sara

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.

Details

Published by: Springer

Publication Date: 2016-06-29

Format: Paperback

ISBN-13: 9783319327730

DOI: 10.1007/978-3-319-32774-7

Dimensions: 235cm x155cm

Pages: 274