Download or read book Spline Smoothing by Generalized Cross validation a Technique for Data Smoothing written by P. H. Merz and published by . This book was released on 1978 with total page 50 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Smoothing Noisy Data with Spline Functions written by Peter Craven and published by . This book was released on 1977 with total page 46 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Smoothing Noisy Data with Multidimensional Splines and Generalized Cross validation written by James George Wendelberger and published by . This book was released on 1982 with total page 396 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Statistical Theory and Computational Aspects of Smoothing written by Wolfgang Härdle and published by Springer Science & Business Media. This book was released on 2013-03-08 with total page 265 pages. Available in PDF, EPUB and Kindle. Book excerpt: One of the main applications of statistical smoothing techniques is nonparametric regression. For the last 15 years there has been a strong theoretical interest in the development of such techniques. Related algorithmic concepts have been a main concern in computational statistics. Smoothing techniques in regression as well as other statistical methods are increasingly applied in biosciences and economics. But they are also relevant for medical and psychological research. Introduced are new developments in scatterplot smoothing and applications in statistical modelling. The treatment of the topics is on an intermediate level avoiding too much technicalities. Computational and applied aspects are considered throughout. Of particular interest to readers is the discussion of recent local fitting techniques.
Download or read book Smoothing Noisy Data with Multidimensionsl Splines and Generalized Cross validation written by James George Wendelberger and published by . This book was released on 1982 with total page 672 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Fast Compact Algorithms and Software for Spline Smoothing written by Howard L. Weinert and published by Springer Science & Business Media. This book was released on 2012-10-12 with total page 53 pages. Available in PDF, EPUB and Kindle. Book excerpt: Fast Compact Algorithms and Software for Spline Smoothing investigates algorithmic alternatives for computing cubic smoothing splines when the amount of smoothing is determined automatically by minimizing the generalized cross-validation score. These algorithms are based on Cholesky factorization, QR factorization, or the fast Fourier transform. All algorithms are implemented in MATLAB and are compared based on speed, memory use, and accuracy. An overall best algorithm is identified, which allows very large data sets to be processed quickly on a personal computer.
Download or read book Nonparametric Regression and Spline Smoothing Second Edition written by Randall L. Eubank and published by CRC Press. This book was released on 1999-02-09 with total page 368 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Smoothing Spline ANOVA Models written by Chong Gu and published by Springer Science & Business Media. This book was released on 2013-01-26 with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Surface Approximation by Spline Smoothing and Generalized Cross validation written by Hongmin Lu and published by . This book was released on 1991 with total page 68 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Nonparametric Regression and Generalized Linear Models written by P.J. Green and published by CRC Press. This book was released on 1993-05-01 with total page 198 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book Smoothing Noisy Data with Multidimensional Splines and Generalized Cross validation written by James George Wendelberger and published by . This book was released on 1982 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Smoothing Splines written by Yuedong Wang and published by CRC Press. This book was released on 2011-06-22 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book Smoothing Spline ANOVA Models written by Chong Gu and published by Springer Science & Business Media. This book was released on 2002-01-08 with total page 312 pages. Available in PDF, EPUB and Kindle. Book excerpt: Smoothing methods are an active area of research. In this book, the author presents a comprehensive treatment of penalty smoothing under a unified framework. Methods are developed for (i) regression with Gaussian and non-Gaussian responses as well as with censored life time 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. 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 clone of the popular S/S- PLUS language.
Download or read book Smoothing Techniques for Curve Estimation written by T. Gasser and published by Springer. This book was released on 2006-12-08 with total page 254 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Influence Diagnostics for the Cross validated Smoothing Parameter in Spline Smoothing written by William Thomas and published by . This book was released on 1990 with total page 22 pages. Available in PDF, EPUB and Kindle. Book excerpt: This paper addresses the problem of influence in estimating the smoothing parameter when fitting a univariate smoothing spline. Diagnostics are presented that can identify observations that locally influence the choice of smoothing parameter by generalized cross-validation, through their case weights or observed responses. Generalized cross-validation and the diagnostic methods are given a frequency interpretation and illustrated with examples.
Download or read book Introduction to Nonparametric Regression written by K. Takezawa and published by John Wiley & Sons. This book was released on 2005-12-02 with total page 566 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Download or read book How to smooth curves and surfaces with splines and crossvalidation written by Grace Wahba and published by . This book was released on 1979 with total page 16 pages. Available in PDF, EPUB and Kindle. Book excerpt: The use of smoothing splines and the method of generlized cross validation (GCV) for smoothing discrete noisy data from an unknown but smooth curve is reviewed. The use of 'plaque mince' or Laplacian smoothing splines with GCV for smoothing discrete noisy data from an unknown but smooth surface is described. A numerical algorithm for this (non-trivial) computational problem is described, and an example from a Monte Carlo study is presented to show how the method works on simulated data. The results are extremely promising. Some design problems are briefly mentioned. Some conjectures are made concerning optimality properties of Laplacian smoothing splines and Laplacian histosplines.
