rq.process.
They represent the fit of a linear conditional quantile function model.
This class of objects is returned from the
rq
function
to represent a fitted linear quantile regression model.
The
"rq.process"
class of objects has
methods for the following generic
functions:
effects
,
formula
,
labels
,
model.frame
,
model.matrix
,
plot
,
predict
,
print
,
print.summary
,
summary
,
The following components must be included in a legitimate
rq.process
object.
These arrays are computed by parametric linear programming methods using the exterior point (simplex-type) methods of the Koenker-d'Orey algorithm based on Barrodale and Roberts median regression algorithm.
[1] Koenker, R.W. and Bassett, G.W. (1978). Regression quantiles,
Econometrica, 46, 33-50.
[2] Koenker, R.W. and d'Orey (1987,1994). Computing Regression Quantiles.
Applied Statistics, 36, 383-393, and 43, 410-414.
[3] Gutenbrunner, C. Jureckova, J. (1991).
Regression quantile and regression rank score process in the
linear model and derived statistics, Annals of Statistics, 20, 305-330.
[4] Gutenbrunner, C., J. Jureckova, Koenker, R. and
Portnoy, S.(1994) "Tests of Linear Hypotheses based on Regression
Rank Scores", Journal of Nonparametric Statistics,
(2), 307-331.
[5] Portnoy, S. (1991). Asymptotic behavior of the number of regression
quantile breakpoints, SIAM Journal of Scientific
and Statistical Computing, 12, 867-883.