Density Estimation for Statistics and Data Analysis (Monographs on Statistics and Applied Probability) (Hardcover)

~ Bernard. W. Silverman (Author) "The probability density function is a fundamental concept in statistics..." (more)
Key Phrases: density estimate constructed, adaptive kernel estimate, neighbour estimate, Prakasa Rao, John Wiley, Sons Ltd (more...)

Editorial Reviews

Review

This well-written and moderately priced volume has removed any excuse for ignorance concerning density estimation on the part of applied statisticians; they will find the style refreshingly down-to-earth, and will value the clearsighted exposition. I thoroughly enjoyed reading it, and can recommend it wholeheartedly.
-Short Book Reviews
Highly recommended.
-Choice

Product Description

Although there has been a surge of interest in density estimation in recent years, much of the published research has been concerned with purely technical matters with insufficient emphasis given to the technique's practical value. Furthermore, the subject has been rather inaccessible to the general statistician.The account presented in this book places emphasis on topics of methodological importance, in the hope that this will facilitate broader practical application of density estimation and also encourage research into relevant theoretical work. The book also provides an introduction to the subject for those with general interests in statistics. The important role of density estimation as a graphical technique is reflected by the inclusion of more than 50 graphs and figures throughout the text.Several contexts in which density estimation can be used are discussed, including the exploration and presentation of data, nonparametric discriminant analysis, cluster analysis, simulation and the bootstrap, bump hunting, projection pursuit, and the estimation of hazard rates and other quantities that depend on the density. This book includes general survey of methods available for density estimation. The Kernel method, both for univariate and multivariate data, is discussed in detail, with particular emphasis on ways of deciding how much to smooth and on computation aspects. Attention is also given to adaptive methods, which smooth to a greater degree in the tails of the distribution, and to methods based on the idea of penalized likelihood.

Product Details

• Hardcover: 176 pages
• Publisher: Chapman & Hall/CRC; 1 edition (April 1, 1986)
• Language: English
• ISBN-10: 0412246201
• ISBN-13: 978-0412246203
• Product Dimensions: 9.1 x 6.3 x 0.6 inches
• Shipping Weight: 13.6 ounces (View shipping rates and policies)
• Average Customer Review: 5.0 out of 5 stars  See all reviews (3 customer reviews)
• Amazon.com Sales Rank: #168,502 in Books (See Bestsellers in Books)

Customer Reviews

Most Helpful Customer Reviews

31 of 32 people found the following review helpful:

5.0 out of 5 stars Best book on this subject, March 6, 2001

By	DataGuru (DC) - See all my reviews

Quite a few books have been written since 1986, but this book is still the best. Very intuitive and very readable. It is written with a mastery of the subject and an excellent style of pedagogy. I remember of the joy and refreshness of reading this book around 1987 and it has served me well on a very important introductory of mordern statistics without having to go through tedious "math" notations and a shining example that statistics can be full of intuitive ideas and beautiful. For people unfamiliar with this book, it deals with probability density estimation using the idea of "local averages", and so it does not deal with other techniques such as splines. Also it is purely a density estimation book, and does not deal with another important problem, namely regression estimation (on which there are many other books). In summary, this book introduces the ideas and sense of "smoothing", a large (perhaps a little overblown) area of modern statistics. If you want to learn statistical smoothing, besides from Steve Marron, this one is the way to go.

27 of 28 people found the following review helpful:

5.0 out of 5 stars beautifully written and concise, January 24, 2008

By	Michael R. Chernick "statman31147" (Holland PA) - See all my reviews (REAL NAME)

I had the good fortune to take a short course from Bernie Silverman on density estimation just after this book came out in 1986. It is one of the clearest treatments of the subject and I found it particularly good on the coverage of optimal kernels.

It is also filled with good practical examples and advice. For instance, the Old Faithful data provides an excellent example of a bimodal distribution where kernel density estimation provides a way to detect the two modes.

The author was also very perceptive in recognizing the value of projection pursuit techniques and bootstrap methods and the way density estimation techniques relate to these methods.

The book has the virtue of being clear and concise.

11 of 11 people found the following review helpful:

5.0 out of 5 stars excellent text on density estimation, April 5, 2001

By	Michael R. Chernick "statman13" (Malvern, PA) - See all my reviews (REAL NAME)

I had the good fortune to take a short course from Bernie Silverman on density estimation just after this book came out in 1986. It is one of the clearest treatments of the subject and I found it particularly good on the coverage of optimal kernels. It is also filled with good practical examples and advice. For instance, the Old Faithful data provides an excellent example of a bimodal distribution where kernel density estimation provides a way to detect the two modes.

The author was also very perceptive in recognizing the value of projection pursuit techniques and bootstrap methods and the way density estimation techniques relate to these methods.

The book has the virtue of being clear and concise.