rq.fit.pfn: Preprocessing Algorithm for Quantile Regression

In quantreg: Quantile Regression

Preprocessing Algorithm for Quantile Regression

Description

A preprocessing algorithm for the Frisch Newton algorithm for quantile regression. This is one possible method for rq().

Usage

rq.fit.pfn(x, y, tau=0.5, Mm.factor=0.8, max.bad.fixups=3, eps=1e-06)

Arguments

x	design matrix usually supplied via rq()
y	response vector usually supplied via rq()
tau	quantile of interest
Mm.factor	constant to determine sub sample size m
max.bad.fixups	number of allowed mispredicted signs of residuals
eps	convergence tolerance

Details

Preprocessing algorithm to reduce the effective sample size for QR problems with (plausibly) iid samples. The preprocessing relies on subsampling of the original data, so situations in which the observations are not plausibly iid, are likely to cause problems. The tolerance eps may be relaxed somewhat.

Value

Returns an object of type rq

Author(s)

Roger Koenker <[e-mail]>

References

Portnoy and Koenker, Statistical Science, (1997) 279-300

See Also

rq

quantreg documentation built on Aug. 19, 2023, 5:09 p.m.