Wolfgang Härdle

Smoothing Techniques (eBook, PDF)

With Implementation in S

• Format: PDF

Produktbeschreibung

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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Produktdetails

• Verlag: Springer US
• Seitenzahl: 262
• Erscheinungstermin: 6. Dezember 2012
• Englisch
• ISBN-13: 9781461244325
• Artikelnr.: 44058313

Autorenporträt

Wolfgang Härdle is a professor of statistics at the Humboldt-Universität zu Berlin and director of C.A.S.E. the Centre for Applied Statistics and Economics. He teaches quantitative finance and semiparametric statistical methods. His research focuses on dynamic factor models, multivariate statistics in finance and computational statistics. He is an elected ISI member and advisor to the Guanghua School of Management, Peking University and to National Central University, Taiwan.

Inhaltsangabe

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.