hbrfit-package: High Breakdown Rank-based (HBR) fitting and inference for...

Description Details Author(s) References Examples

Description

High breakdown rank-based (HBR) estimates are robust to outliers in both X & Y spaces. They are based on a weighted Wilcoxon pseudo-norm. Data points which are outliers in both X & Y space are downweighted. HBR estimates achieve 50

This package is based on the weighted Wilcoxon (ww) code developed by Terpstra and McKean (2005) under GPL.

Details

Package: hbrfit
Type: Package
Version: 0.01
Date: 2013-10-30
License: GPL

Author(s)

Jeff Terpstra, Joe McKean, John Kloke Maintainer: John Kloke [email protected]

References

Chang, W. McKean, J.W., Naranjo, J.D., and Sheather, S.J. (1999), High breakdown rank-based regression, Journal of the American Statistical Association, 94, 205-219.

Hettmansperger, T.P. and McKean J.W. (2011), Robust Nonparametric Statistical Methods, 2nd ed., New York: Chapman-Hall.

Terpstra, J. and McKean, J.W. (2005), Rank-based analyses of linear models using R, Journal of Statistical Software, 14(7).

Examples

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data(stars)
plot(stars)
fit<-hbrfit(light~temperature,data=stars)
abline(fit)
summary(fit)

kloke/hbrfit documentation built on May 18, 2017, 9:41 p.m.