QR.lasso.admm: Quantile Regression (QR) with Adaptive Lasso Penalty (lasso)...

In cqrReg: Quantile, Composite Quantile Regression and Regularized Versions

Quantile Regression (QR) with Adaptive Lasso Penalty (lasso) use Alternating Direction Method of Multipliers (ADMM) algorithm

Description

The adaptive lasso parameter base on the estimated coefficient without penalty function. The problem is well suited to distributed convex optimization and is based on Alternating Direction Method of Multipliers (ADMM) algorithm .

Usage

QR.lasso.admm(X,y,tau,lambda,rho,beta,maxit)

Arguments

X	the design matrix
y	response variable
tau	quantile level
lambda	The constant coefficient of penalty function. (default lambda=1)
rho	augmented Lagrangian parameter
beta	initial value of estimate coefficient (default naive guess by least square estimation)
maxit	maxim iteration (default 200)

Value

a list structure is with components

beta	the vector of estimated coefficient
b	intercept

Note

QR.lasso.admm(x,y,tau) work properly only if the least square estimation is good.

References

S. Boyd, N. Parikh, E. Chu, B. Peleato and J. Eckstein.(2010) Distributed Optimization and Statistical Learning via the Alternating Direction. Method of Multipliers Foundations and Trends in Machine Learning, 3, No.1, 1–122

Wu, Yichao and Liu, Yufeng (2009). Variable selection in quantile regression. Statistica Sinica, 19, 801–817.

Examples

set.seed(1)
n=100
p=2
a=2*rnorm(n*2*p, mean = 1, sd =1)
x=matrix(a,n,2*p)
beta=2*rnorm(p,1,1)
beta=rbind(matrix(beta,p,1),matrix(0,p,1))
y=x%*%beta-matrix(rnorm(n,0.1,1),n,1)
# x is 1000*20 matrix, y is 1000*1 vector, beta is 20*1 vector with last ten zero value elements. 
QR.lasso.admm(x,y,0.1)

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