Nonparametric regression and generalized linear models
Nonparametric regression and generalized linear models
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DOI：
10.1007/978-1-4899-4473-3
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发表时间：
1994
期刊：
影响因子：
--
通讯作者：
Peter J. Green;Bernard Walter Silverman
中科院分区：
文献类型：
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作者：
Peter J. Green;Bernard Walter Silverman
近年来，在统计学中非参数平滑的一般领域引起了极大的兴趣和活跃。这本专著集中于粗糙度惩罚方法，并展示了该技术如何提供了一种统一的方法来解决广泛的平滑问题。该方法允许在回归问题中、在通过广义线性建模接近的问题中以及在许多其他情况下实现参数假设。贯穿始终的重点是方法论而不是理论，它集中在统计和计算问题上。用实际数据实例说明了各种方法，并将它们与标准参数方法进行了比较。文中还讨论了一些公开可用的软件。数学处理是自成体系的，主要依靠简单的线性代数和微积分。这本专著将作为研究和应用统计学家的参考著作，并作为研究生和其他第一次遇到这些材料的人的文本。
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.