A Distribution-Free Theory of Nonparametric Regression | 
 enlarge | Authors: Laszlo Gyoerfi, Michael Kohler, Adam Krzyzak, Harro Walk
Publisher: Springer
Category: Book
List Price: $115.00
Buy New: $99.40
You Save: $15.60 (14%)
New (15) Used (8) from $99.40
Sales Rank: 1389660
Media: Hardcover
Edition: 1
Pages: 656
Number Of Items: 1
Shipping Weight (lbs): 2
Dimensions (in): 9.2 x 6.3 x 1.7
ISBN: 0387954414
Dewey Decimal Number: 519.536
EAN: 9780387954417
Publication Date: August 12, 2002
Availability: Usually ships in 1-2 business days
Shipping: Expedited shipping available
Shipping: International shipping available
Condition: New American book. Printed on demand and shipped within the US in 4-7 days (expedited) or about 10-14 days (standard). Standard can occasionally be slower so we advise using expedited if quicker delivery is important!
| |
| Similar Items:
|
| Editorial Reviews:
Product Description
This book provides a systematic in-depth analysis of nonparametric regression with random design. It covers almost all known estimates such as classical local averaging estimates including kernel, partitioning and nearest neighbor estimates, least squares estimates using splines, neural networks and radial basis function networks, penalized least squares estimates, local polynomial kernel estimates, and orthogonal series estimates. The emphasis is on distribution-free properties of the estimates. Most consistency results are valid for all distributions of the data. Whenever it is not possible to derive distribution-free results, as in the case of the rates of convergence, the emphasis is on results which require as few constrains on distributions as possible, on distribution-free inequalities, and on adaptation. The relevant mathematical theory is systematically developed and requires only a basic knowledge of probability theory. The book will be a valuable reference for anyone interested in nonparametric regression and is a rich source of many useful mathematical techniques widely scattered in the literature. In particular, the book introduces the reader to empirical process theory, martingales and approximation properties of neural networks.
|
|
|
