Nonparametric Regression and Generalized Linear Models 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024

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P.J. Green 作者

Chapman and Hall/CRC

译者

1993-5-1 出版日期

184 页数

GBP 124.00 价格

Hardcover

丛书系列

9780412300400 图书编码

Nonparametric Regression and Generalized Linear Models 在线电子书图书标签: 统计数学光滑样条 textbook統計 Statistics GCV Data-Mining @網

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Nonparametric Regression and Generalized Linear Models 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

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这本书讲得非常的通俗易懂，我认为学习光滑样条和GCV的话还是这本书适合入门。其它教程太偏重数学了，普通理工科背景的人很难读懂，而这本书只需要线性代数和初等微积分的知识。

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from Genton 12.1 Nonparametric Regression and Generalized Linear Models - Green and Silverman (Chapman & Hall CRC, 1994) 不全

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from Genton 12.1 Nonparametric Regression and Generalized Linear Models - Green and Silverman (Chapman & Hall CRC, 1994) 不全

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from Genton 12.1 Nonparametric Regression and Generalized Linear Models - Green and Silverman (Chapman & Hall CRC, 1994) 不全

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Nonparametric Regression and Generalized Linear Models 在线电子书著者简介

Nonparametric Regression and Generalized Linear Models 在线电子书图书目录

Nonparametric Regression and Generalized Linear Models 在线电子书 pdf 下载 txt下载 epub 下载 mobi 在线电子书下载

Nonparametric Regression and Generalized Linear Models 在线电子书图书描述

In recent years, there has been a great deal of interest and activity in the general area of nonparametric smoothing in statistics. This monograph concentrates on the roughness penalty method and shows how this technique provides a unifying approach to a wide range of smoothing problems. The method allows parametric assumptions to be realized in regression problems, in those approached by generalized linear modelling, and in many other contexts. The emphasis throughout is methodological rather than theoretical, and it concentrates on statistical and computation issues. Real data examples are used to illustrate the various methods and to compare them with standard parametric approaches. Some publicly available software is also discussed. The mathematical treatment is self-contained and depends mainly on simple linear algebra and calculus. This monograph will be useful both as a reference work for research and applied statisticians and as a text for graduate students and other encountering the material for the first time.

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