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Discrete And Continuous Non Parametric Smoothing Spline Regression And Dependent Errors
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Book Synopsis Discrete and Continuous Non-parametric Smoothing Spline Regression and Dependent Errors by : Michael Georg Schimek
Download or read book Discrete and Continuous Non-parametric Smoothing Spline Regression and Dependent Errors written by Michael Georg Schimek and published by . This book was released on 1991 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Nonparametric Regression and Spline Smoothing, Second Edition by : Randall L. Eubank
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.
Book Synopsis Applied Nonparametric Regression by : Wolfgang Härdle
Download or read book Applied Nonparametric Regression written by Wolfgang Härdle and published by Cambridge University Press. This book was released on 1990 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is the first book to bring together in one place the techniques for regression curve smoothing involving more than one variable.
Book Synopsis Mathematical Approaches to Brain Functioning Diagnostics by : Ivan Dvorak (Ed)
Download or read book Mathematical Approaches to Brain Functioning Diagnostics written by Ivan Dvorak (Ed) and published by Manchester University Press. This book was released on 1991 with total page 488 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Spline Smoothing and Nonparametric Regression by : Randall L. Eubank
Download or read book Spline Smoothing and Nonparametric Regression written by Randall L. Eubank and published by . This book was released on 1988 with total page 476 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Nonparametric Regression and Spline Smoothing by : Randall L. Eubank
Download or read book Nonparametric Regression and Spline Smoothing written by Randall L. Eubank and published by . This book was released on 1999 with total page 338 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
Book Synopsis Smoothing and Regression by : Michael G. Schimek
Download or read book Smoothing and Regression written by Michael G. Schimek and published by John Wiley & Sons. This book was released on 2013-05-29 with total page 682 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive introduction to a wide variety of univariate and multivariate smoothing techniques for regression Smoothing and Regression: Approaches, Computation, and Application 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 technical and specialized, this book discusses all methods in light of both computational efficiency and their applicability for real data analysis. Using examples of applications from the biosciences, environmental sciences, engineering, and economics, as well as medical research and marketing, this volume addresses the theory, computation, and application of each approach. A number of the techniques discussed, such as smoothing under shape restrictions or of dependent data, are presented for the first time in book form. Special features of this book include: * Comprehensive coverage of smoothing and regression with software hints and applications from a wide variety of disciplines * A unified, easy-to-follow format * Contributions from more than 25 leading researchers from around the world * More than 150 illustrations also covering new graphical techniques important for exploratory data analysis and visualization of high-dimensional problems * Extensive end-of-chapter references For professionals and aspiring professionals in statistics, applied mathematics, computer science, and econometrics, as well as for researchers in the applied and social sciences, Smoothing and Regression is a unique and important new resource destined to become one the most frequently consulted references in the field.
Book Synopsis Variable Selection for Multivariate Smoothing Splines with Correlated Random Errors by : Eren Demirhan
Download or read book Variable Selection for Multivariate Smoothing Splines with Correlated Random Errors written by Eren Demirhan and published by . This book was released on 2008 with total page 163 pages. Available in PDF, EPUB and Kindle. Book excerpt: Keywords: smoothing spline ANOVA, COSSO, LASSO, nonparametric regression, variable selection, correlated data.
Book Synopsis A Distribution-Free Theory of Nonparametric Regression by : László Györfi
Download or read book A Distribution-Free Theory of Nonparametric Regression written by László Györfi and published by Springer Science & Business Media. This book was released on 2006-04-18 with total page 662 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a systematic in-depth analysis of nonparametric regression with random design. It covers almost all known estimates. The emphasis is on distribution-free properties of the estimates.
Book Synopsis Testing for No Effect in Nonparametric Regression Via Spline Smoothing Techniques by : Juei-Chao Chen
Download or read book Testing for No Effect in Nonparametric Regression Via Spline Smoothing Techniques written by Juei-Chao Chen and published by . This book was released on 1992 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt: We propose three statistics for testing that a predictor variable has no effect on the response variable in regression analysis. The test statistics are integrals of squared derivatives of various orders of a periodic smoothing spline fit to the data. The large sample properties of the test statistics are investigated under the null hypothesis and sequences of local alternatives and a Monte Carlo study is conducted to assess finite sample power properties.
Book Synopsis A Smoothing Spline Based Test of Model Adequacy in Nonparametric Regression by : Eunmee Koh
Download or read book A Smoothing Spline Based Test of Model Adequacy in Nonparametric Regression written by Eunmee Koh and published by . This book was released on 1989 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Nonparametric Regression with Dependent Errors by : Yuhong Yang
Download or read book Nonparametric Regression with Dependent Errors written by Yuhong Yang and published by . This book was released on 1997 with total page 33 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Nonparametric Regression Methods for Longitudinal Data Analysis by : Hulin Wu
Download or read book Nonparametric Regression Methods for Longitudinal Data Analysis written by Hulin Wu and published by John Wiley & Sons. This book was released on 2006-05-12 with total page 401 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Nonparametric Spline Regression with Autoregressive Moving Average Errors by : Robert Kohn
Download or read book Nonparametric Spline Regression with Autoregressive Moving Average Errors written by Robert Kohn and published by . This book was released on 1991 with total page 23 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Nonparametric Econometrics by : Qi Li
Download or read book Nonparametric Econometrics written by Qi Li and published by Princeton University Press. This book was released on 2023-07-18 with total page 768 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive, up-to-date textbook on nonparametric methods for students and researchers Until now, students and researchers in nonparametric and semiparametric statistics and econometrics have had to turn to the latest journal articles to keep pace with these emerging methods of economic analysis. Nonparametric Econometrics fills a major gap by gathering together the most up-to-date theory and techniques and presenting them in a remarkably straightforward and accessible format. The empirical tests, data, and exercises included in this textbook help make it the ideal introduction for graduate students and an indispensable resource for researchers. Nonparametric and semiparametric methods have attracted a great deal of attention from statisticians in recent decades. While the majority of existing books on the subject operate from the presumption that the underlying data is strictly continuous in nature, more often than not social scientists deal with categorical data—nominal and ordinal—in applied settings. The conventional nonparametric approach to dealing with the presence of discrete variables is acknowledged to be unsatisfactory. This book is tailored to the needs of applied econometricians and social scientists. Qi Li and Jeffrey Racine emphasize nonparametric techniques suited to the rich array of data types—continuous, nominal, and ordinal—within one coherent framework. They also emphasize the properties of nonparametric estimators in the presence of potentially irrelevant variables. Nonparametric Econometrics covers all the material necessary to understand and apply nonparametric methods for real-world problems.
Book Synopsis A Correction to 'Generalized Nonparametric Smoothing With Mixed Discrete and Continuous Data' by Li, Simar & Zelenyuk by : Jeffrey Racine
Download or read book A Correction to 'Generalized Nonparametric Smoothing With Mixed Discrete and Continuous Data' by Li, Simar & Zelenyuk written by Jeffrey Racine and published by . This book was released on 2016 with total page 33 pages. Available in PDF, EPUB and Kindle. Book excerpt: Li & Racine (2004) have proposed a nonparametric kernel-based method for smoothing in the presence of categorical predictors as an alternative to the classical nonparametric approach that splits the data into subsets ('cells') defined by the unique combinations of the categorical predictors. Li, Simar & Zelenyuk (2014) present an alternative to Li & Racine's (2004) method that they claim possesses lower mean square error and generalizes and improves upon the existing approaches. However, these claims do not appear to withstand scrutiny. A number of points need to be brought to the attention of practitioners, and two in particular stand out; a) Li et al.'s (2014) own simulation results reveal that their estimator performs worse than the existing classical 'split' estimator and appears to be inadmissible, and b) the claim that Li et al.'s (2014) estimator dominates that of Li & Racine (2004) on mean square error grounds does not appear to be the case. The classical split estimator and that of Li & Racine (2004) are both consistent, and it will be seen that Li & Racine's (2004) estimator remains the best all around performer. And, as a practical matter, Li et al.'s (2014) estimator is not a feasible alternative in typical settings involving multinomial and multiple categorical predictors.
