02813nam 22005655 450 991095862530332120250813224830.01-4612-4432-310.1007/978-1-4612-4432-5(CKB)3400000000090838(SSID)ssj0001298480(PQKBManifestationID)11766356(PQKBTitleCode)TC0001298480(PQKBWorkID)11261817(PQKB)11076838(DE-He213)978-1-4612-4432-5(MiAaPQ)EBC3077148(PPN)238056635(EXLCZ)99340000000009083820121227d1991 u| 0engurnn|008mamaatxtccrSmoothing Techniques With Implementation in S /by Wolfgang Härdle1st ed. 1991.New York, NY :Springer New York :Imprint: Springer,1991.1 online resource (XII, 262 p.) Springer Series in Statistics,2197-568X"With 87 Illustrations."0-387-97367-2 1-4612-8768-5 Includes bibliographical references and index.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.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.Springer Series in Statistics,2197-568XMathematicsApplications of MathematicsMathematics.Applications of Mathematics.519Härdle Wolfgangauthttp://id.loc.gov/vocabulary/relators/aut420941MiAaPQMiAaPQMiAaPQBOOK9910958625303321Smoothing techniques808737UNINA