Wasserman Larry. All of Nonparametric Statistics

Springer, 2006. — 280 p.There are many books on various aspects of nonparametric inference such as density estimation, nonparametric regression, bootstrapping, and wavelets methods. But it is hard to find all these topics covered in one place. The goal of this text is to provide the reader with a single book where they can find a brief account of many of the modern topics in nonparametric inference.
The book is aimed at Master’s level or Ph.D. level statistics and computer science students. It is also suitable for researchers in statistics, machine learning and data mining who want to get up to speed quickly on modern nonparametric methods. My goal is to quickly acquaint the reader with the basic concepts in many areas rather than tackling any one topic in great detail. In the interest of covering a wide range of topics, while keeping the book short, I have opted to omit most proofs. Bibliographic remarks point the reader to references which contain further details. Of course, I have had to choose topics to include and to omit, the title notwithstanding. For the most part, I decided to omit topics that are too big to cover in one chapter. For example, I do not cover classification or nonparametric Bayesian inference.