Nonparametric Expectile Regression and Testing
Title | Nonparametric Expectile Regression and Testing PDF eBook |
Author | Seoghoon Kang |
Publisher | |
Pages | 298 |
Release | 1991 |
Genre | |
ISBN |
Testing for Additivity in Nonparametric Quantile Regression
Title | Testing for Additivity in Nonparametric Quantile Regression PDF eBook |
Author | Holger Dette |
Publisher | |
Pages | 0 |
Release | 2011 |
Genre | |
ISBN |
Nonparametric Regression and Spline Smoothing
Title | Nonparametric Regression and Spline Smoothing PDF eBook |
Author | Randall L. Eubank |
Publisher | CRC Press |
Pages | 359 |
Release | 1999-02-09 |
Genre | Mathematics |
ISBN | 1482273144 |
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 co
Nonparametric Tests for Regression Models
Title | Nonparametric Tests for Regression Models PDF eBook |
Author | Shishirkumar Shreedhar Jogdeo |
Publisher | |
Pages | 116 |
Release | 1962 |
Genre | |
ISBN |
A Distribution-Free Theory of Nonparametric Regression
Title | A Distribution-Free Theory of Nonparametric Regression PDF eBook |
Author | László Györfi |
Publisher | Springer Science & Business Media |
Pages | 662 |
Release | 2006-04-18 |
Genre | Mathematics |
ISBN | 0387224424 |
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.
Testing a Parametric Quantile-regression Model with an Endogenous Explanatory Variable Against a Nonparametric Alternative
Title | Testing a Parametric Quantile-regression Model with an Endogenous Explanatory Variable Against a Nonparametric Alternative PDF eBook |
Author | |
Publisher | |
Pages | |
Release | 2007 |
Genre | |
ISBN |
Introduction to Nonparametric Regression
Title | Introduction to Nonparametric Regression PDF eBook |
Author | K. Takezawa |
Publisher | John Wiley & Sons |
Pages | 566 |
Release | 2005-12-02 |
Genre | Mathematics |
ISBN | 0471771449 |
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.