Ebook Download Smoothing Methods in Statistics (Springer Series in Statistics), by Jeffrey S. Simonoff
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Smoothing Methods in Statistics (Springer Series in Statistics), by Jeffrey S. Simonoff
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Focussing on applications, this book covers a very broad range, including simple and complex univariate and multivariate density estimation, nonparametric regression estimation, categorical data smoothing, and applications of smoothing to other areas of statistics. It will thus be of particular interest to data analysts, as arguments generally proceed from actual data rather than statistical theory, while the "Background Material" sections will interest statisticians studying the field. Over 750 references allow researchers to find the original sources for more details, and the "Computational Issues" sections provide sources for statistical software that use the methods discussed. Each chapter includes exercises with a heavily computational focus based upon the data sets used in the book, making it equally suitable as a textbook for a course in smoothing.
- Sales Rank: #3252565 in Books
- Brand: Brand: Springer
- Published on: 1998-05-28
- Original language: English
- Number of items: 1
- Dimensions: 9.21" h x .81" w x 6.14" l, 1.47 pounds
- Binding: Hardcover
- 340 pages
- Used Book in Good Condition
Most helpful customer reviews
0 of 0 people found the following review helpful.
Overpriced, not enough content
By complexsystems
Simonoff's text is at best an overview of the methods in non-parametric kernel smoothing techniques as of its writing. However, the text is very short, and omits any sort of aids to actually understand how some of the asymptotic properties were derived, for which outside sources are required. This was particularly troublesome as before hand I had never done any asymptotic expansions, so not seeing how it was done was confusing without class lectures or supplementary texts. In the later category, Hansen's nonparametric lecture notes and Qi Li's "Nonparametric Econometrics," seem to cover mostly the same material while not omitting so much detail. On these grounds, I would rate the book at best a 2/5 stars.
HOWEVER, Simonoff does have interesting "extra tidbits," at the end of each chapter which discuss non-trivial extensions, additional work, and history that is not covered in some of the supplementary materials mentioned above, and is a unique addition to some of the other statistical reference and text books I've bought recently, for which the book gets an additional star. However, almost on pure content grounds I am still stuck between removing it for being charged $180+ for the text.
4 of 6 people found the following review helpful.
Very good book
By Paulo Q. Saraiva
This book is very good. The motivation for the issues on nonparametric methods a excellent. The only reason why I haven't given it 5 stars is that I would like to see more precise proofs.
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