Estimation and Testing Under Sparsity: École dÉté de Probabilités de Saint-Flour XLV – 2015

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.

• ISBN: 978-3-319-32773-0
• Editorial: Springer
• Encuadernacion: Rústica
• Páginas: 274
• Fecha Publicación: 29/06/2016
• Nº Volúmenes: 1
• Idioma: Inglés