Description
Aimed at Masters or PhD level students in statistics, computer science, and engineering, this comprehensive text provides the reader with a single book where they can find accounts of a number of up-to-date issues in nonparametric inference, all set out with exceptional clarity. It is also suitable for researchers who want to get up to speed quickly on modern nonparametric methods. With an exhaustive exploration of asymptotic nonparametric inferences, it also covers a huge range of other crucial topic areas including the bootstrap, the nonparametric delta method, nonparametric regression, density estimation, orthogonal function methods, minimax estimation, nonparametric confidence sets, and wavelets. The book's dual approach includes a mixture of methodology and theory.
Author: Larry Wasserman
Publisher: Springer
Published: 10/21/2005
Pages: 270
Binding Type: Hardcover
Weight: 1.16lbs
Size: 9.58h x 6.30w x 0.74d
ISBN13: 9780387251455
ISBN10: 0387251456
BISAC Categories:
- Mathematics | Probability & Statistics | General
- Computers | Artificial Intelligence | General
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