Nonparametric Regression And Spline Smoothing Second Edition PDF Download
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Author | : Randall L. Eubank |
Publisher | : CRC Press |
Total Pages | : 368 |
Release | : 1999-02-09 |
Genre | : Mathematics |
ISBN | : 9780824793371 |
Download Nonparametric Regression and Spline Smoothing, Second Edition Book in PDF, ePub and Kindle
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.
Author | : Randall L. Eubank |
Publisher | : |
Total Pages | : 338 |
Release | : 1999 |
Genre | : |
ISBN | : |
Download Nonparametric Regression and Spline Smoothing Book in PDF, ePub and Kindle
Author | : P.J. Green |
Publisher | : CRC Press |
Total Pages | : 198 |
Release | : 1993-05-01 |
Genre | : Mathematics |
ISBN | : 9780412300400 |
Download Nonparametric Regression and Generalized Linear Models Book in PDF, ePub and Kindle
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.
Author | : Randall L. Eubank |
Publisher | : |
Total Pages | : 476 |
Release | : 1988 |
Genre | : Mathematics |
ISBN | : |
Download Spline Smoothing and Nonparametric Regression Book in PDF, ePub and Kindle
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.
Author | : Yuedong Wang |
Publisher | : CRC Press |
Total Pages | : 380 |
Release | : 2011-06-22 |
Genre | : Computers |
ISBN | : 1420077562 |
Download Smoothing Splines Book in PDF, ePub and Kindle
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
Author | : K. Takezawa |
Publisher | : John Wiley & Sons |
Total Pages | : 566 |
Release | : 2005-12-02 |
Genre | : Mathematics |
ISBN | : 0471771449 |
Download Introduction to Nonparametric Regression Book in PDF, ePub and Kindle
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.
Author | : Hulin Wu |
Publisher | : John Wiley & Sons |
Total Pages | : 401 |
Release | : 2006-05-12 |
Genre | : Mathematics |
ISBN | : 0470009667 |
Download Nonparametric Regression Methods for Longitudinal Data Analysis Book in PDF, ePub and Kindle
Incorporates mixed-effects modeling techniques for more powerful and efficient methods This book presents current and effective nonparametric regression techniques for longitudinal data analysis and systematically investigates the incorporation of mixed-effects modeling techniques into various nonparametric regression models. The authors emphasize modeling ideas and inference methodologies, although some theoretical results for the justification of the proposed methods are presented. With its logical structure and organization, beginning with basic principles, the text develops the foundation needed to master advanced principles and applications. Following a brief overview, data examples from biomedical research studies are presented and point to the need for nonparametric regression analysis approaches. Next, the authors review mixed-effects models and nonparametric regression models, which are the two key building blocks of the proposed modeling techniques. The core section of the book consists of four chapters dedicated to the major nonparametric regression methods: local polynomial, regression spline, smoothing spline, and penalized spline. The next two chapters extend these modeling techniques to semiparametric and time varying coefficient models for longitudinal data analysis. The final chapter examines discrete longitudinal data modeling and analysis. Each chapter concludes with a summary that highlights key points and also provides bibliographic notes that point to additional sources for further study. Examples of data analysis from biomedical research are used to illustrate the methodologies contained throughout the book. Technical proofs are presented in separate appendices. With its focus on solving problems, this is an excellent textbook for upper-level undergraduate and graduate courses in longitudinal data analysis. It is also recommended as a reference for biostatisticians and other theoretical and applied research statisticians with an interest in longitudinal data analysis. Not only do readers gain an understanding of the principles of various nonparametric regression methods, but they also gain a practical understanding of how to use the methods to tackle real-world problems.
Author | : Chong Gu |
Publisher | : Springer Science & Business Media |
Total Pages | : 446 |
Release | : 2013-01-26 |
Genre | : Mathematics |
ISBN | : 1461453690 |
Download Smoothing Spline ANOVA Models Book in PDF, ePub and Kindle
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.
Author | : Wolfgang Härdle |
Publisher | : Cambridge University Press |
Total Pages | : 356 |
Release | : 1990 |
Genre | : Business & Economics |
ISBN | : 9780521429504 |
Download Applied Nonparametric Regression Book in PDF, ePub and Kindle
This is the first book to bring together in one place the techniques for regression curve smoothing involving more than one variable.
Author | : Craig F. Ansley |
Publisher | : |
Total Pages | : 25 |
Release | : 1992 |
Genre | : Spline theory |
ISBN | : 9781862741638 |
Download Nonparametric Spline Regression with Prior Information Book in PDF, ePub and Kindle