hbrwts: Returns the weights for HBR regression.

In kloke/hbrfit: High breakdown rank-based (HBR) estimation for linear models

Returns the weights for HBR regression.

Description

Returns the weights for HBR regression.

Usage

hbrwts(x, y, percent = 0.95, ehat0 = ltsreg(x, y)$residuals)

Arguments

x	n by p design matrix
y	n by 1 vector of responses
percent	percentile of chi^2 dist to be used as a cut-off
ehat0	initial residuals

Details

Used internally in hbrfit. For details see references below.

Author(s)

Jeff Terpstra, Joe McKean, John Kloke

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).

See Also

hbrfit

kloke/hbrfit documentation built on Nov. 17, 2023, 2:33 p.m.