All of Nonparametric Statistics

by Larry Wasserman

This text provides the reader with a single book where they can find accounts of a number of up-to-date issues in nonparametric inference. The book is aimed at Masters or PhD level students in statistics, computer science, and engineering. It is also suitable for researchers who want to get up to speed quickly on modern nonparametric methods. It covers a wide range of topics 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.

Genres: Mathematics, Textbooks, Technical, Artificial Intelligence, Nonfiction

282 pages, Kindle Edition

First published October 1, 2005

About the author

Larry A. Wasserman is a Canadian statistician and a professor in the Department of Statistics and the Machine Learning Department at Carnegie Mellon University.

Reviews

[Anurag · Rating 5 out of 5]

Not my favourite genre of Statistics books as it states theorems without proofs., However, the elegance and simplicity of explanations in the book is so exceptional that despite being thrown theorems and equations, one forgets that this is a real mathematical text and has seemingly complex topics. It is hard for me to think of any other textbook that has introduced kernel smoothing methods and density estimation with such ease.

[Chris · Rating 2 out of 5]

downvoted because the topic is pointless. Statisticians will twaddle for eternity debating frivolous issues such as Epanechnikov vs triangular "kernel" … for how to plot a continuous version of a histogram. Value-less scholasticism.

[Travis · Rating 5 out of 5]

Shocked! As a statistics graduates who feel inadequate about math, I found this book bring me a lot of intuition/understanding of statistics. Although I have read a lot of statistics textbooks or reference books before, I still feel shocked by this book.

Instead of theorems with solid proofs, author focus on the Zen of statistics. This book is not for someone wants to use statitics as a tool, but for someone wants to experience the "Art of Statistics".

[Laura · Rating 2 out of 5]

This book would make a good reference, but it's not a good book to learn from.