cqr.fit.lasso: Composite Quantile Regression (cqr) with Adaptive Lasso...

In cqrReg: Quantile, Composite Quantile Regression and Regularized Versions

Composite Quantile Regression (cqr) with Adaptive Lasso Penalty (lasso)

Description

Composite quantile regression (cqr) find the estimated coefficient which minimize the absolute error for various quantile level. High level function for estimating and selecting parameter by composite quantile regression with adaptive lasso penalty.

Usage

cqr.fit.lasso(X,y,tau,lambda,beta,method,maxit,toler,rho)

Arguments

X	the design matrix
y	response variable
tau	vector of quantile level
method	"mm" for majorize and minimize method,"cd" for coordinate descent method, "admm" for Alternating method of mulipliers method
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)
toler	the tolerance critical for stop the algorithm (default 1e-3)

Value

a list structure is with components

beta	the vector of estimated coefficient
b	intercept

Note

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

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