Iterative bias reduction multivariate smoothing in R: The ibr package

Details Iterative bias reduction multivariate smoothing in R: The ibr package Iterative bias reduction multivariate smoothing in R: The ibr package

2011-05-18

arxiv.org/abs/1105.3605v1

Statistics

Computation

20 pages, 7 figures

Scientific paper

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.

Affiliated with

Also associated with

No associations

Rating

Iterative bias reduction multivariate smoothing in R: The ibr package does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Iterative bias reduction multivariate smoothing in R: The ibr package, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Iterative bias reduction multivariate smoothing in R: The ibr package will most certainly appreciate the feedback.

Rate now

Comments { 0 }

Profile ID: LFWR-SCP-O-53995