R中的迭代偏差减少多元平滑：ibr软件包
Iterative bias reduction multivariate smoothing in R: The ibr package
摘要：
在多元非参数分析中，协变量的稀疏性也称为维数灾难，迫使人们使用大的平滑参数。这会导致一个有偏的平滑器。而不是专注于最佳选择平滑参数，我们将其固定为某个合理的大值，以确保数据的过度平滑。生成的基础平滑器的方差很小，但偏差很大。在本文中，我们提出了一个R软件包名为ibr迭代校正的初始偏差的（基地）估计的偏差通过平滑残差获得的估计。在简单描述了迭代偏差减少平滑器之后，我们研究了包中实现的基本平滑器：Nadaraya-Watson内核平滑器和薄板样条平滑器。然后，我们解释包中可用的停止规则及其实现。最后，我们用两个例子来说明这个包：RxR中的一个玩具例子和原始的洛杉矶臭氧数据集。
英文摘要：
In multivariate nonparametric analysis, sparseness of the covariates also called curse of dimensionality, forces one to use large smoothing parameters. This leads to a biased smoother. Instead of focusing on optimally selecting the smoothing parameter, we fix it to some reasonably large value to ensure an over-smoothing of the data. The resulting base smoother has a small variance but a substantial bias. In this paper, we propose an R package named ibr to iteratively correct the initial bias of the (base) estimator by an estimate of the bias obtained by smoothing the residuals. After a brief description of Iterated Bias Reduction smoothers, we examine the base smoothers implemented in the packages: Nadaraya-Watson kernel smoothers and thin plate splines smoothers. Then, we explain the stopping rules available in the package and their implementation. Finally we illustrate the package on two examples: a toy example in RxR and the original Los Angeles ozone dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
