Kernel Smoothing (Chapman & Hall/CRC Monographs on Statistics & Applied Probability Book 60) 1st Edition by M.P. Wand (PDF)

Description

Kernel smoothing refers to a general methodology for recovery of underlying structure in data sets. The basic principle is that local averaging or smoothing is performed with respect to a kernel function.This book provides uninitiated readers with a feeling for the principles, applications, and analysis of kernel smoothers. This is facilitated by the authors’ focus on the simplest settings, namely density estimation and nonparametric regression. They pay particular attention to the problem of choosing the smoothing parameter of a kernel smoother, and also treat the multivariate case in detail. Kernal Smoothing is self-contained and assumes only a basic knowledge of statistics, calculus, and matrix algebra. It is an invaluable introduction to the main ideas of kernel estimation for students and researchers from other discipline and provides a comprehensive reference for those familiar with the topic.

User’s Reviews

Reviews from Amazon users which were colected at the time this book was published on the website:

⭐I knew this book was a classic in the field. I didn’t know it was as good as it truly is. The authors are clearly gifted teachers, because from the very beginning they provide very understandable explanations and examples. The mathematics is not elementary, but is kept to a level that ought to make the material accessible to any statistician as well as researchers who want to learn about kernel smoothers. The book was written in the mid 1990s, and should be considered an introduction.

⭐Great reference for kernel density estimation or kernel regression. Even though it is not the newest book, it still is a great text and contains all the information you need to understand this topic..

⭐not that helpful

⭐This book is an excellent introduction to Kernel Smoothing. Its easy to follow and I strongly recommend it.

⭐If your work requires the use of kernel density methods, there are three books you must have: (1) Density Estimation by Silverman, (2) Multivariate Density Estimation by David Scott, and (3) Kernel smoothing by Wand & Jones. Of the three, Wand & Jones’ book is the best written one. Concepts are explained clearly and the book chapters are organized well. Strongly recommended alongwith the other two.

⭐This is by far the best written and most accessible statistics book I have ever read. A basic statistics background is adequate for following the material, and the (I know I come back to this) writing just makes sense! There’s an appendix on computational aspects of kernel smoothing, which I found very useful.

⭐Ich beschäftige mich in letzter Zeit – just for fun – mit einem Fussball-Wettbot namens Zoccer. Enpassant erweitere ich meinen Statistik-Werkzeugkasten. Kernel-Smoothing gehört aber immer schon zu den Lieblingsmethoden.Es ist KISS. Ich habe mir erhofft neue Verfahren kennen zu lernen. Das war nicht der Fall. Das Buch liefert die mathematischen Grundlagen. In dieser Hinsicht ist es sicher ausgezeichnet. Aus Sicht des Statistik-Handwerkers bringt es aber eher weniger. Die im Detail behandelten Verfahren zur automatischen Bestimmung der Bandwidth sind theoretischer Natur. Es geht in dieser Hinsicht nichts über ein geschultes Auge.Ein thematisch ähnliches Buch ist: L. Wasserman: All of Nonparametric Statistics. Wasserman ist mathematisch weniger rigoros (es enthält aber immer noch eine hohe Dosis Mathematik), inhaltlich aber umfangreicher. Wasserman hat mich mehr angesprochen.

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