Preprocessing method for fitting quantile regression models that exploits the fact that adjacent tau's should have nearly the same sign vectors for residuals.
rq.fit.ppro(x, y, tau, weights = NULL, Mm.factor = 0.8, eps = 1e-06, ...)Design matrix
Response vector
quantile vector of interest
case weights
constant determining initial sample size
Convergence tolerance
Other arguments
Returns a list with components:
Matrix of coefficient estimates
Matrix of residual estimates
vector of objective function values
vector of case weights
See references for further details.
Chernozhukov, V. I. Fernandez-Val and B. Melly, Fast Algorithms for the Quantile Regression Process, 2020, Empirical Economics.,
Portnoy, S. and R. Koenker, The Gaussian Hare and the Laplacian Tortoise, Statistical Science, (1997) 279-300