Nonparametric models and their estimation
Nonparametric models and their estimation
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DOI：
10.1007/s10182-006-0226-0
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发表时间：
2006-03
期刊：
影响因子：
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通讯作者：
G. Kauermann
中科院分区：
文献类型：
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作者：
G. Kauermann
非参数模型在过去的二十年里变得越来越流行。其流行的一个原因是软件可用性，这很容易允许将平滑但未指定的函数拟合到数据中。模型的一个好处是，回归函数的函数形状不是预先指定的，而是由数据确定的。显然，这允许更多的洞察力，可以解释的物质物质的水平。本文给出了可用的拟合例程，通常称为平滑程序的概述。此外，一些经典的散点图平滑扩展进行了讨论，与例子支持的例程的优点。
Nonparametric models have become more and more popular over the last two decades. One reason for their popularity is software availability, which easily allows to fit smooth but otherwise unspecified functions to data. A benefit of the models is that the functional shape of a regression function is not prespecified in advance, but determined by the data. Clearly this allows for more insight which can be interpreted on a substance matter level.This paper gives an overview of available fitting routines, commonly called smoothing procedures. Moreover, a number of extensions to classical scatterplot smoothing are discussed, with examples supporting the advantages of the routines.