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Smoothing Techniques : With Implementation in S

Author: Wolfgang Härdle
Publisher: New York, NY : Springer New York, 1991.
Series: Springer series in statistics.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
The author has attempted to present a book that provides a non-technical introduction into the area of non-parametric density and regression function estimation. The application of these methods is discussed in terms of the S computing environment. Smoothing in high dimensions faces the problem of data sparseness. A principal feature of smoothing, the averaging of data points in a prescribed neighborhood, is not  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Wolfgang Härdle
ISBN: 9781461244325 1461244323
OCLC Number: 852791697
Description: 1 online resource (xi, 261 pages 87 illustrations).
Contents: Preface --
Density Smoothing: The Histogram. Kernel Density Estimation. Further Density Estimators. Bandwidth Selection in Practice --
Regression Smoothing: Nonparametric Regression. Bandwidth Selection. Simultaneous Error Bars --
Appendix.
Series Title: Springer series in statistics.
Responsibility: by Wolfgang Härdle.

Abstract:

A principal feature of smoothing, the averaging of data points in a prescribed neighborhood, is not really practicable in dimensions greater than three if we have just one hundred data points.  Read more...

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