| Smoothing Methods in Statistics 1996. Corr. 2nd Edition Contributor(s): Simonoff, Jeffrey S. (Author) |
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ISBN: 0387947167 ISBN-13: 9780387947167 Publisher: Springer OUR PRICE: $170.99 Product Type: Hardcover Published: June 1996 Annotation: This book surveys the uses of smoothing methods in statistics. The coverage has an applied focus and is very broad, including simple and complex univariate and multivariate density estimation, nonparametric regression estimation, k categorical data smoothing, and applications of smoothing to other areas of statistics. |
| Additional Information |
| BISAC Categories: - Science - Mathematics | Probability & Statistics - General |
| Dewey: 519.536 |
| LCCN: 96011742 |
| Series: Springer Series in Statistics |
| Physical Information: 0.94" H x 6.37" W x 9.52" (1.47 lbs) 340 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: The existence of high speed, inexpensive computing has made it easy to look at data in ways that were once impossible. Where once a data analyst was forced to make restrictive assumptions before beginning, the power of the computer now allows great freedom in deciding where an analysis should go. One area that has benefited greatly from this new freedom is that of non parametric density, distribution, and regression function estimation, or what are generally called smoothing methods. Most people are familiar with some smoothing methods (such as the histogram) but are unlikely to know about more recent developments that could be useful to them. If a group of experts on statistical smoothing methods are put in a room, two things are likely to happen. First, they will agree that data analysts seriously underappreciate smoothing methods. Smoothing meth- ods use computing power to give analysts the ability to highlight unusual structure very effectively, by taking advantage of people's abilities to draw conclusions from well-designed graphics. Data analysts should take advan- tage of this, they will argue. |