This textbook by Larry Wasserman covers modern topics in nonparametric inference. It provides a concise introduction to methods such as density estimation, nonparametric regression, bootstrapping, and wavelets. This reference work is suitable for master’s and PhD students in statistics and computer science, as well as for researchers who want to quickly gain insight into current nonparametric techniques.
Contents
There are many books on various aspects of nonparametric inference, but few bring these topics together in a clear and comprehensive way in a single work. This book focuses on a broad introduction without in-depth mathematical proofs, so the reader can quickly understand familiar basic concepts. Topics such as classification and nonparametric Bayesian inference are not covered due to their scope. The text is based on a twenty-hour course for primarily master’s students.
Product specifications
- Author: Larry Wasserman
- Series: Springer Texts in Statistics
- Publisher: Springer-Verlag New York Inc.
- Publication date: 2005-10-21
- Number of pages: 270
- ISBN: 9780387251455
- Subject: Probability and statistics
- BISAC: MATHEMATICS / Probability & Statistics / General