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Smoothing and regression : approaches, computation, and application

Author: Michael G Schimek; Wiley InterScience (Online service)
Publisher: New York : Wiley, ©2000.
Series: Wiley series in probability and statistics., Applied probability and statistics.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
A comprehensive introduction to a wide variety of univariate and multivariate smoothing techniques for regression, this volume bridges the many gaps that exist among competing univariate and multivariate smoothing techniques. It introduces, describes, and in some cases compares a large number of the latest and most advanced techniques for regression modeling. Unlike many other volumes on this topic, which are highly  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Smoothing and regression.
New York : Wiley, ©2000
(OCoLC)681875925
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Michael G Schimek; Wiley InterScience (Online service)
ISBN: 9781118150658 1118150651 9781118150641 1118150643
OCLC Number: 779616111
Notes: "A Wiley-Interscience Publication."
Reproduction Notes: Electronic reproduction. [S.l.] : HathiTrust Digital Library, 2010. MiAaHDL
Description: 1 online resource (xix, 607 pages) : illustrations.
Details: Master and use copy. Digital master created according to Benchmark for Faithful Digital Reproductions of Monographs and Serials, Version 1. Digital Library Federation, December 2002.
Contents: Spline regression / Randall L. Eubank --
Variance estimation and smoothing-parameter selection for spline regression / Angelika van der Linde --
Kernel regression / Pascal Sarda, Philippe Vieu --
Variance estimation and bandwidth selection for kernel regression / Eva Herrmann --
Spline and kernel regression under shape restrictions / Michel Delecroix, Christine Thomas-Agnan --
Spline and kernel regression for dependent data / Robert Kohn, Michael G. Schimek, Michael Smith --
Wavelets for regression and other statistical problems / Guy P. Nason and Bernard W. Silverman --
Smoothing methods for discrete data / Jeffrey S. Simonoff and Gerhard Tutz --
Local polynomial fitting / Jianqing Fan and Irene Gijbels --
Additive and generalized additive models / Michael G. Schimek and Berwin A. Turlach --
Multivariate spline regression / Chong Gu --
Multivariate and semiparametric kernel regression / Wolfgang Härdie and Marlene Müller --
Spatial-process estimates as smoothers / Douglas W. Nychka --
Resampling methods for nonparametric regression / Enno Mammen --
Multidimensional smoothing and visualization / David W. Scott --
Projection pursuit regression / Sigbert Klinke and Janet Grassmann --
Sliced inverse regression / Thomas T. Kötter --
Dynamic and semiparametric models / Ludwig Fahrmeir and Leonhard Knorr-Held --
Nonparametric Bayesian bivariate surface estimation / Michael Smith, Robert Kohn, and Paul Yau.
Series Title: Wiley series in probability and statistics., Applied probability and statistics.
Responsibility: edited by Michael G. Schimek.
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Abstract:

Introducing smoothing techniques (splines and kernels) necessary for non- and semi-parametric regression, this collection discusses a variety of approaches to multivariate regression problems,  Read more...

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From the publishers description: "...a unique and important new resource destined to become on of the most frequently consulted references in the field." (Mathematical Reviews, 2001 f) "...provides a Read more...

 
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