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.
General
| Imprint: |
Springer International Publishing AG
|
| Country of origin: |
Switzerland |
| Series: |
Ecole d'Ete de Probabilites de Saint-Flour, 2159 |
| Release date: |
July 2016 |
| First published: |
2016 |
| Authors: |
Sara Van De Geer
|
| Dimensions: |
235 x 155 x 21mm (L x W x T) |
| Format: |
Paperback
|
| Pages: |
274 |
| Edition: |
1st ed. 2016 |
| ISBN-13: |
978-3-319-32773-0 |
| Categories: |
Books >
Science & Mathematics >
Mathematics >
Probability & statistics
Promotions
|
| LSN: |
3-319-32773-9 |
| Barcode: |
9783319327730 |
Is the information for this product incomplete, wrong or inappropriate?
Let us know about it.
Does this product have an incorrect or missing image?
Send us a new image.
Is this product missing categories?
Add more categories.
Review This Product
No reviews yet - be the first to create one!
Welcome to Loot.co.za!
Sign in / Register |Wishlists & Gift Vouchers |Help | Advanced search
