Smoothing Techniques : With Implementation in S / by Wolfgang Härdle.

Format:

Book

Author/Creator:

Härdle, Wolfgang, Author.

Series:

Springer Series in Statistics, 2197-568X

Language:

English

Subjects (All):

Mathematics.

Applications of Mathematics.

Local Subjects:

Applications of Mathematics.

Physical Description:

1 online resource (XII, 262 p.)

Edition:

1st ed. 1991.

Place of Publication:

New York, NY : Springer New York : Imprint: Springer, 1991.

Language Note:

English

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 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.

Contents:

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.

Notes:

"With 87 Illustrations."

Includes bibliographical references and index.

ISBN:

1-4612-4432-3