cqr.ip {cqrReg}

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)