非参数经典书 a distribution free Theory of Nonparametic Regression

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This book provides a systematic in-depth analysis of nonparametric regression with random design. It covers almost all known estimates such as classical local averaging estimates including kernel, partitioning and nearest neighbor estimates, least squares estimates using splines, neural networks and radial basis function networks, penalized least squares estimates, local polynomial kernel estimates, and orthogonal series estimates. The emphasis is on distribution-free properties of the estimates. Most consistency results are valid for all distributions of the data. Whenever it is not possible to derive distribution-free results, as in the case of the rates of convergence, the emphasis is on results which require as few constrains on distributions as possible, on distribution-free inequalities, and on adaptation. The relevant mathematical theory is systematically developed and requires only a basic knowledge of probability theory. The book will be a valuable reference for anyone interested in nonparametric regression and is a rich source of many useful mathematical techniques widely scattered in the literature. In particular, the book introduces the reader to empirical process theory, martingales and approximation properties of neural networks.

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关键词：distribution Nonparametic regression regressio nonpara orthogonal including function emphasis provides

很不错的一本书，写的很全也很详细，希望对大家有帮助！

谢谢提供，对我很还有帮助

thanks
1# changxinfeng

好书，值得一看，内容不是很新了......

下来看看，先谢过LZ　

xialaikankan
xiexie

thanks for your sharing~~

挺不错啊啊