# rq.fit.pfn

0th

Percentile

##### Preprocessing Algorithm for Quantile Regression

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

Keywords
regression
##### Usage
rq.fit.pfn(x, y, tau=0.5, Mm.factor=0.8, max.bad.fixup=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

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

##### References

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

rq