Estimation and Testing Under Sparsity : Ecole d'Ete de Probabilites de Saint-Flour XLV – 2015
1st ed. 2016
Book Details
Format
Paperback / Softback
Book Series
Lecture Notes in Mathematics
ISBN-10
3319327739
ISBN-13
9783319327730
Edition
1st ed. 2016
Publisher
Springer International Publishing AG
Imprint
Springer International Publishing AG
Country of Manufacture
CH
Country of Publication
GB
Publication Date
Jun 29th, 2016
Print length
274 Pages
Weight
436 grams
Dimensions
23.40 x 15.40 x 2.10 cms
Product Classification:
Probability & statisticsProbability and statistics
Ksh 9,000.00
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Taking the Lasso method as its starting point, this book describes the main ingredients needed to study general loss functions and sparsity-inducing regularizers. Examples include the Lasso and group Lasso methods, and the least squares method with other norm-penalties, such as the nuclear norm.
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.
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