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Semiparametric Regression.
~
Carroll, R. J.
Semiparametric Regression.
Record Type:
Electronic resources : Monograph/item
Title/Author:
Semiparametric Regression./
Author:
Ruppert, David.
other author:
Carroll, R. J.
Published:
Cambridge :Cambridge University Press, : 2003.,
Description:
404 p.
Online resource:
Click here to view book
ISBN:
9780511755453 (electronic bk.)
Semiparametric Regression.
Ruppert, David.
Semiparametric Regression.
[electronic resource]. - Cambridge :Cambridge University Press,2003. - 404 p.
Cover; Half-title; Series-title; Title; Copyright; Dedication; Contents; Preface; Guide to Notation; 1 Introduction; 2 Parametric Regression; 3 Scatterplot Smoothing; 4 Mixed Models; 5 Automatic Scatterplot Smoothing; 6 Inference; 7 Simple Semiparametric Models; 8 Additive Models; 9 Semiparametric Mixed Models; 10 Generalized Parametric Regression; 11 Generalized Additive Models; 12 Interaction Models; 13 Bivariate Smoothing; 14 Variance Function Estimation; 15 Measurement Error; 16 Bayesian Semiparametric Regression; 17 Spatially Adaptive Smoothing; 18 Analyses; 19 Epilogue
Assuming only a basic familiarity with ordinary parametric regression, this user-friendly book explains the techniques and benefits of semiparametric regression in a concise and modular fashion. The authors make liberal use of graphics and examples plus case studies taken from environmental, financial, and other applications. They include practical advice on implementation and pointers to relevant software.
Electronic reproduction.
Available via World Wide Web.
Mode of access: World Wide Web.
ISBN: 9780511755453 (electronic bk.)Index Terms--Genre/Form:
96803
Electronic books.
LC Class. No.: Q278.2 . / R87 2003
Dewey Class. No.: 519.5/36221
Semiparametric Regression.
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9780521780506 (print)
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Semiparametric Regression.
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[electronic resource].
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2003.
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Cambridge University Press,
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404 p.
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Cover; Half-title; Series-title; Title; Copyright; Dedication; Contents; Preface; Guide to Notation; 1 Introduction; 2 Parametric Regression; 3 Scatterplot Smoothing; 4 Mixed Models; 5 Automatic Scatterplot Smoothing; 6 Inference; 7 Simple Semiparametric Models; 8 Additive Models; 9 Semiparametric Mixed Models; 10 Generalized Parametric Regression; 11 Generalized Additive Models; 12 Interaction Models; 13 Bivariate Smoothing; 14 Variance Function Estimation; 15 Measurement Error; 16 Bayesian Semiparametric Regression; 17 Spatially Adaptive Smoothing; 18 Analyses; 19 Epilogue
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A Technical ComplementsB Computational Issues; Bibliography; Author Index; Notation Index; Example Index; Subject Index
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Assuming only a basic familiarity with ordinary parametric regression, this user-friendly book explains the techniques and benefits of semiparametric regression in a concise and modular fashion. The authors make liberal use of graphics and examples plus case studies taken from environmental, financial, and other applications. They include practical advice on implementation and pointers to relevant software.
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Carroll, R. J.
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Gill, R.
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Ripley, B. D.
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Ross, S.
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Stein, M.
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Wand, M. P.
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Williams, D.
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Click here to view book
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http://ebooks.cambridge.org/ebook.jsf?bid=CBO9780511755453
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