cqr.ip: Composite Quantile Regression (cqr) use Interior Point (ip)...

In cqrReg: Quantile, Composite Quantile Regression and Regularized Versions

Composite Quantile Regression (cqr) use Interior Point (ip) Method

Description

The function use the interior point method from quantreg to solve the quantile regression problem.

Usage

cqr.ip(X,y,tau)

Arguments

X	the design matrix
y	response variable
tau	vector of quantile level

Value

a list structure is with components

beta	the vector of estimated coefficient
b	intercept

Note

Need to install quantreg package from CRAN.

References

Koenker, R. and S. Portnoy (1997). The Gaussian Hare and the Laplacian Tortoise: Computability of squared-error vs. absolute-error estimators, with discussion, Statistical Science, 12, 279-300.

Hui Zou and Ming Yuan(2008). Composite Quantile Regression and the Oracle Model Selection Theory, The Annals of Statistics, 36, Number 3, Page 1108–1126.

Examples

set.seed(1)
n=100
p=2
a=rnorm(n*p, mean = 1, sd =1)
x=matrix(a,n,p)
beta=rnorm(p,1,1)
beta=matrix(beta,p,1)
y=x%*%beta-matrix(rnorm(n,0.1,1),n,1)
tau=1:5/6
# x is 1000*10 matrix, y is 1000*1 vector, beta is 10*1 vector
#you should install quantreg first to run following command
#cqr.ip(x,y,tau)

cqrReg documentation built on June 7, 2022, 9:06 a.m.