Introduction to Nonparametric Regression
โ Scribed by K. Takezawa
- Publisher
- Wiley-Interscience
- Year
- 2006
- Tongue
- English
- Leaves
- 557
- Series
- Wiley series in probability and statistics
- Edition
- 1
- Category
- Library
No coin nor oath required. For personal study only.
โฆ Synopsis
Written for undergraduate and graduate courses, this text takes a step-by-step approach and assumes students have only a basic knowledge of linear algebra and statistics. The explanations therefore avoid complex mathematics and excessive abstract theory, and even statistical information is accompanied by clear numerical examples and equations are explained all the way through the process. Topics include smoothing out data with an equispaced predictor, nonparametric regression for a one-dimensional predictor, multidimensional smoothing, nonparametric regression with predictors represented as distributions, smoothing of histograms and nonparametric probability density functions and pattern recognition. Each chapter includes exercises.
๐ SIMILAR VOLUMES
<p><P>Methods of nonparametric estimation are located at the core of modern statistical science. The aim of this book is to give a short but mathematically self-contained introduction to the theory of nonparametric estimation. The emphasis is on the construction of optimal estimators; therefore the
"This book presents the theory and practice of non-parametric statistics, with an emphasis on motivating principals. The course is a combination of traditional rank based methods and more computationally-intensive topics like density estimation, kernel smoothers in regression, and robustness. The te