LassoLambdaHat: Lambda selection for QR lasso problems

In quantreg: Quantile Regression

Lambda selection for QR lasso problems

Description

Default procedure for selection of lambda in lasso constrained quantile regression as proposed by Belloni and Chernozhukov (2011)

Usage

LassoLambdaHat(X, R = 1000, tau = 0.5, C = 1, alpha = 0.95)

Arguments

X	Design matrix
R	Number of replications
tau	quantile of interest
C	Cosmological constant
alpha	Interval threshold

Details

As proposed by Belloni and Chernozhukov, a reasonable default lambda would be the upper quantile of the simulated values. The procedure is based on idea that a simulated gradient can be used as a pivotal statistic. Elements of the default vector are standardized by the respective standard deviations of the covariates. Note that the sqrt(tau(1-tau)) factor cancels in their (2.4) (2.6). In this formulation even the intercept is penalized. If the lower limit of the simulated interval is desired one can specify alpha = 0.05.

Value

vector of default lambda values of length p, the column dimension of X.

References

Belloni, A. and V. Chernozhukov. (2011) l1-penalized quantile regression in high-dimensional sparse models. Annals of Statistics, 39 82 - 130.

Examples

n <- 200
p <- 10
x <- matrix(rnorm(n*p), n, p)
b <- c(1,1, rep(0, p-2))
y <- x %*% b + rnorm(n)
f <- rq(y ~ x, tau = 0.8, method = "lasso")
# See f$lambda to see the default lambda selection

quantreg documentation built on Oct. 22, 2024, 5:07 p.m.