Smoothing Techniques : With Implementation in S

Wolfgang Härdle

ISBN 10: 1461287685 / ISBN 13: 9781461287681

Verlag: Springer New York, 2011

Beschreibung

Druck auf Anfrage Neuware - Printed after ordering - 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.

Bestandsnummer des Verkäufers 9781461287681

Bibliografische Details

• Titel: Smoothing Techniques : With Implementation in S
• Verlag: Springer New York
• Erscheinungsjahr: 2011
• Sprache: Englisch
• ISBN-10: 1461287685
• ISBN-13: 9781461287681
• Einband: Taschenbuch
• Zustand: Neu

Über diesen Titel

Inhaltsangabe

The book is addressed to both the theoretical and applied statistician. It can be used as an undergraduate text.

Reseña del editor

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.