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" Smoothing Techniques "
by Wolfgang Härdle.
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BL
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| Record Number
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573757
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b402976
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| Main Entry
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Härdle, Wolfgang.
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| Title & Author
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Smoothing Techniques : With Implementation in S /\ by Wolfgang Härdle.
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| Publication Statement
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New York, NY :: Springer New York,, 1991.
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| Series Statement
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Springer Series in Statistics,
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| ISBN
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9781461244325
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: 9781461287681
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| Contents
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I. Density Smoothing -- 1. The Histogram -- 2. Kernel Density Estimation -- 3. Further Density Estimators -- 4. Bandwidth Selection in Practice -- II. Regression Smoothing -- 5. Nonparametric Regression -- 6. Bandwidth Selection -- 7. Simultaneous Error Bars -- Tables -- Solutions -- List of Used S Commands -- Symbols and Notation -- References.
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| Abstract
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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 really practicable in dimensions greater than three if we have just one hundred data points. Additive models provide a way out of this dilemma; but, for their interactiveness and recursiveness, they require highly effective algorithms. For this purpose, the method of WARPing (Weighted Averaging using Rounded Points) is described in great detail.
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| Subject
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Statistics.
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SpringerLink (Online service)
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