Nonparametric Regression And Spline Smoothing Second Edition
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Nonparametric Regression And Spline Smoothing Second Edition
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Author : Randall L. Eubank
language : en
Publisher: CRC Press
Release Date : 1999-02-09
Nonparametric Regression And Spline Smoothing Second Edition written by Randall L. Eubank and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999-02-09 with Mathematics categories.
Provides a unified account of the most popular approaches to nonparametric regression smoothing. This edition contains discussions of boundary corrections for trigonometric series estimators; detailed asymptotics for polynomial regression; testing goodness-of-fit; estimation in partially linear models; practical aspects, problems and methods for confidence intervals and bands; local polynomial regression; and form and asymptotic properties of linear smoothing splines.
Nonparametric Regression And Spline Smoothing
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Author : Randall L. Eubank
language : en
Publisher:
Release Date : 1999
Nonparametric Regression And Spline Smoothing written by Randall L. Eubank and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999 with categories.
Nonparametric Regression And Generalized Linear Models
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Author : P.J. Green
language : en
Publisher: CRC Press
Release Date : 1993-05-01
Nonparametric Regression And Generalized Linear Models written by P.J. Green and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1993-05-01 with Mathematics categories.
In recent years, there has been a great deal of interest and activity in the general area of nonparametric smoothing in statistics. This monograph concentrates on the roughness penalty method and shows how this technique provides a unifying approach to a wide range of smoothing problems. The method allows parametric assumptions to be realized in regression problems, in those approached by generalized linear modelling, and in many other contexts. The emphasis throughout is methodological rather than theoretical, and it concentrates on statistical and computation issues. Real data examples are used to illustrate the various methods and to compare them with standard parametric approaches. Some publicly available software is also discussed. The mathematical treatment is self-contained and depends mainly on simple linear algebra and calculus. This monograph will be useful both as a reference work for research and applied statisticians and as a text for graduate students and other encountering the material for the first time.
Spline Smoothing And Nonparametric Regression
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Author : Randall L. Eubank
language : en
Publisher:
Release Date : 1988
Spline Smoothing And Nonparametric Regression written by Randall L. Eubank and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1988 with Mathematics categories.
Regression analysis; Nonparametric regression; Scope; What is a good estimator? Function spaces and series estimators; Kernel estimators; Smoothing splines; Smoothing splines: extensions and asymptotic theory; Least-squares splines and other estimators; Linear and nonlinear regression; Linear models; Nonlinear models; Bayesian interpretations and inference.
Smoothing Splines
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Author : Yuedong Wang
language : en
Publisher: CRC Press
Release Date : 2011-06-22
Smoothing Splines written by Yuedong Wang and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-06-22 with Computers categories.
A general class of powerful and flexible modeling techniques, spline smoothing has attracted a great deal of research attention in recent years and has been widely used in many application areas, from medicine to economics. Smoothing Splines: Methods and Applications covers basic smoothing spline models, including polynomial, periodic, spherical, t
Introduction To Nonparametric Regression
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Author : K. Takezawa
language : en
Publisher: John Wiley & Sons
Release Date : 2005-12-02
Introduction To Nonparametric Regression written by K. Takezawa and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-12-02 with Mathematics categories.
An easy-to-grasp introduction to nonparametric regression This book's straightforward, step-by-step approach provides an excellent introduction to the field for novices of nonparametric regression. Introduction to Nonparametric Regression clearly explains the basic concepts underlying nonparametric regression and features: * Thorough explanations of various techniques, which avoid complex mathematics and excessive abstract theory to help readers intuitively grasp the value of nonparametric regression methods * Statistical techniques accompanied by clear numerical examples that further assist readers in developing and implementing their own solutions * Mathematical equations that are accompanied by a clear explanation of how the equation was derived The first chapter leads with a compelling argument for studying nonparametric regression and sets the stage for more advanced discussions. In addition to covering standard topics, such as kernel and spline methods, the book provides in-depth coverage of the smoothing of histograms, a topic generally not covered in comparable texts. With a learning-by-doing approach, each topical chapter includes thorough S-Plus? examples that allow readers to duplicate the same results described in the chapter. A separate appendix is devoted to the conversion of S-Plus objects to R objects. In addition, each chapter ends with a set of problems that test readers' grasp of key concepts and techniques and also prepares them for more advanced topics. This book is recommended as a textbook for undergraduate and graduate courses in nonparametric regression. Only a basic knowledge of linear algebra and statistics is required. In addition, this is an excellent resource for researchers and engineers in such fields as pattern recognition, speech understanding, and data mining. Practitioners who rely on nonparametric regression for analyzing data in the physical, biological, and social sciences, as well as in finance and economics, will find this an unparalleled resource.
Applied Nonparametric Regression
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Author : Wolfgang Härdle
language : en
Publisher: Cambridge University Press
Release Date : 1990
Applied Nonparametric Regression written by Wolfgang Härdle and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1990 with Business & Economics categories.
This is the first book to bring together in one place the techniques for regression curve smoothing involving more than one variable.
Nonparametric Spline Regression With Prior Information
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Author : Craig F. Ansley
language : en
Publisher:
Release Date : 1992
Nonparametric Spline Regression With Prior Information written by Craig F. Ansley and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992 with Spline theory categories.
Smoothing Spline Anova Models
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Author : Chong Gu
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-01-26
Smoothing Spline Anova Models written by Chong Gu and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-01-26 with Mathematics categories.
Nonparametric function estimation with stochastic data, otherwise known as smoothing, has been studied by several generations of statisticians. Assisted by the ample computing power in today's servers, desktops, and laptops, smoothing methods have been finding their ways into everyday data analysis by practitioners. While scores of methods have proved successful for univariate smoothing, ones practical in multivariate settings number far less. Smoothing spline ANOVA models are a versatile family of smoothing methods derived through roughness penalties, that are suitable for both univariate and multivariate problems. In this book, the author presents a treatise on penalty smoothing under a unified framework. Methods are developed for (i) regression with Gaussian and non-Gaussian responses as well as with censored lifetime data; (ii) density and conditional density estimation under a variety of sampling schemes; and (iii) hazard rate estimation with censored life time data and covariates. The unifying themes are the general penalized likelihood method and the construction of multivariate models with built-in ANOVA decompositions. Extensive discussions are devoted to model construction, smoothing parameter selection, computation, and asymptotic convergence. Most of the computational and data analytical tools discussed in the book are implemented in R, an open-source platform for statistical computing and graphics. Suites of functions are embodied in the R package gss, and are illustrated throughout the book using simulated and real data examples. This monograph will be useful as a reference work for researchers in theoretical and applied statistics as well as for those in other related disciplines. It can also be used as a text for graduate level courses on the subject. Most of the materials are accessible to a second year graduate student with a good training in calculus and linear algebra and working knowledge in basic statistical inferences such as linear models and maximum likelihood estimates.
Nonparametric Simple Regression
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Author : John Fox
language : en
Publisher: SAGE
Release Date : 2000-01-20
Nonparametric Simple Regression written by John Fox and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000-01-20 with Social Science categories.
Nonparametric simple regression forms the basis for nonparametric multiple regression and directly supplies the building blocks for the type of nonparametric multiple regression called additive regression.