【书名】 INTRODUCTION TO NONPARAMETRIC REGRESSION
【作者】 Takezawa, Kunio
【出版社】 John Wiley & Sons, Inc., Hoboken, New Jersey.
【版本】 2006年第二版 （2001年日文版）
【出版日期】 2006
【文件格式】djvu
【文件大小】3.57MB
【页数】 557
【ISBN出版号】 ISBN-13 978-0-471-74583-9
ISBN-10 0-471-74583-9
【资料类别】计量经济学，统计学 专著
【市面定价】
【扫描版还是影印版】影印版
【是否缺页】完整
【关键词】 regression analysis; Nonparametric statistics; S-plus
【内容简介】this book aims at helping readers to understand the basic concepts underlying nonparametric regression in order to utilize it practically, even if they are scarcely acquainted with this field and are reading this book on their own initiative with only a fundamental grasp of statistics and the rudiments of linear algebra. Therefore, this book commits itself to providing explanations in plain language by avoiding complex mathematics and excessively generalized expressions in order to allow the value and attractiveness of nonparametric regression to come across intuitively.
【目录】
1 Exordium
1.1 Introduction
1.2 Are the moving average and Fourier series sufficiently useful?
1.3 Is a histogram or normal distribution sufficiently powerful?
1.4 Is interpolation sufficiently powerful?
1.5 Should we use a descriptive equation?
1.6 Parametric regression and nonparametric regression
2 Smoothing for data with an equispaced predictor
2.1 Introduction
2.2 Moving average and binomial filter
2.3 Hat matrix
2.4 Local linear regression
2.5 Smoothing spline
2.6 Analysis on eigenvalue of hat matrix
2.7 Examples of S-Plus object
3 Nonparametric regression for one-dimensional predictor
3.1 Introduction
3.2 Trade-off between bias and variance
3.3 Index to select beneficial regression equations
3.4 Nadaraya-Watson estimator
3.5 Local polynomial regression
3.6 Natural spline and smoothing spline
3.7 LOESS
3.8 Supersmoother
3.9 LOWESS
3.10 Examples of S-Plus object
4 Multidimensional smoothing
4.1 Introduction
4.2 Local polynomial regression for multidimensional predictor
4.3 Thin plate smoothing splines
4.4 LOESS and LOWESS with plural predictors
4.5 Kriging
4.6 Additive model
4.7 ACE
4.8 Projection pursuit regression
4.9 Examples of S-Plus object
5 Nonparametric regression with predictors represented as distributions
5.1 Introduction
5.2 Use of distributions as predictors
5.3 Nonparametric DVR method
5.4 Form of nonparametric regression with predictors represented as distributions
5.5 Examples of S-Plus object
6 Smoothing of histograms and nonparametric probability density functions
6.1 Introduction
6.2 Histogram
6.3 Smoothing a histogram
6.4 Nonparametric probability density function
6.5 Examples of S-Plus object
7 Pattern recognition
7.1 Introduction
7.2 Bayes' decision rule
7.3 Linear discriminant rule and quadratic discriminant rule
7.4 Classification using nonparametric probability density function
7.5 Logistic regression
7.6 Neural networks
7.7 Tree-based model
7.8 k-nearest-neighbor classifier
7.9 Nonparametric regression based on the least squares
7.10 Transformation of feature vectors
7.11 Examples of S-Plus object
Appendix A: Creation and applications of B-spline bases
A.1 Introduction
A.2 Method to create B-spline basis
A.3 Natural spline created by B-spline
A.4 Application to smoothing spline
A.5 Examples of S-Plus object
References
Appendix B: R objects
B.I Introduction
B.2 Transformation of S-Plus objects in Chapter 2
B.3 Transformation of S-Plus objects in Chapter 3
B.4 Transformation of S-Plus objects in Chapter 4
B.5 Transformation of S-Plus objects in Chapter 5
B.6 Transformation of S-Plus objects in Chapter 6
B.7 Transformation of S-Plus objects in Chapter 7
【原创书评】
这本书是我07年11月全面搜罗非参数、半参数计量经济学书籍并开始学习S-plus的时候找到的一本书，也是我看过的关于非参数计量方面的第二本书，（第一本是在07年6月份在上海财大复印的Ruppert的关于样条估计的理论的一本书）。可以说，这本书对我的启发非常大。首先是书中对各种非参数估计方法的基本思想的解释非常到位，我认为对于初学者非常有帮助，感谢yiyo900在论坛上免费分享给大家。尤其值得称道的是，其中的关于各种非参数估计以及推断的 S-plus程序相当多，而且详细，对我学习S-plus起到极大的促进作用（我的第一篇论文中的simulation中关于bandwidth的选择的程序是在这里拷过去略作修改完成的），程序可以直接复制运行。
建议版主可以将该书归到s-plus的书籍类中去。
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