Global and local distance-based generalized linear models
Global and local distance-based generalized linear models
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DOI：
10.1007/s11749-015-0447-1
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发表时间：
2016-03-01
期刊：
影响因子：
1.3
通讯作者：
Fortiana, Josep
中科院分区：
文献类型：
--
作者：
Boj, Eva;Caballe, Adria;Fortiana, Josep
本文介绍了基于局部距离的广义线性模型。这些模型首先将基于距离的线性模型扩展到广义线性模型框架。然后，这些模型的非参数版本提出了通过局部拟合。个体之间的距离是拟合这些模型所需的唯一预测信息。因此，除其他外，它们适用于混合（定性和定量）解释变量或回归变量为函数类型时。R包dbstats提供了一个实现，它还实现了其他基于距离的预测方法。这篇文章的补充材料可以在网上找到，它复制了这篇文章的所有结果。
This paper introduces local distance-based generalized linear models. These models extend (weighted) distance-based linear models first to the generalized linear model framework. Then, a nonparametric version of these models is proposed by means of local fitting. Distances between individuals are the only predictor information needed to fit these models. Therefore, they are applicable, among others, to mixed (qualitative and quantitative) explanatory variables or when the regressor is of functional type. An implementation is provided by the R package dbstats, which also implements other distance-based prediction methods. Supplementary material for this article is available online, which reproduces all the results of this article.