boot.rq.pwxy {quantreg} | R Documentation |
Preprocessing weighted bootstrap method
Description
Bootstrap method exploiting preprocessing strategy to reduce
computation time for large problem. In contrast to
boot.rq.pxy
which uses the classical multinomial
sampling scheme and is coded in R, this uses the exponentially
weighted bootstrap scheme and is coded in fortran and consequently
is considerably faster in larger problems.
Usage
boot.rq.pwxy(x, y, tau, coef, R = 200, m0 = NULL, eps = 1e-06, ...)
Arguments
x |
Design matrix |
y |
response vector |
tau |
quantile of interest |
coef |
point estimate of fitted object |
R |
the number of bootstrap replications desired. |
m0 |
constant to determine initial sample size, defaults to sqrt(n*p) but could use some further tuning... |
eps |
tolerance for convergence of fitting algorithm |
... |
other parameters not yet envisaged. |
Details
The fortran implementation is quite similar to the R code for
boot.rq.pxy
except that there is no multinomial sampling.
Instead rexp(n)
weights are used.
Value
returns a list with elements:
coefficients |
a matrix of dimension ncol(x) by R |
nit |
a 5 by m matrix of iteration counts |
info |
an m-vector of convergence flags |
Author(s)
Blaise Melly and Roger Koenker
References
Chernozhukov, V. I. Fernandez-Val and B. Melly, Fast Algorithms for the Quantile Regression Process, 2019, arXiv, 1909.05782,
Portnoy, S. and R. Koenker, The Gaussian Hare and the Laplacian Tortoise, Statistical Science, (1997) 279-300