Springer Series in Statistics: With Implementation in S

Härdle, Wolfgang

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.

Details

Published by: Springer

Publication Date: 1990-12-05

Format: Hardcover

ISBN-13: 9780387973678

DOI: 10.1007/978-1-4612-4432-5

Dimensions: 234cm x156cm

Pages: 262