Author	Hรคrdle, Wolfgang. author
Title	Smoothing Techniques [electronic resource] : With Implementation in S / by Wolfgang Hรคrdle
Imprint	New York, NY : Springer New York, 1991
Connect to	http://dx.doi.org/10.1007/978-1-4612-4432-5
Descript	XII, 262 p. online resource

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

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

1. Mathematics
2. Applied mathematics
3. Engineering mathematics
4. Mathematics
5. Applications of Mathematics