thresh_est

a vector where the function is to be evaluated.

a number representing a tuning parameter.

lambda

the penalty parameter in the ADMM method.

the tuning parameter of the MCP/SCAD penalty (see details).
Default is 3 for MCP and 3.7 for SCAD.

The penalty to be applied to the model. Either "lasso" (the default), "SCAD",
or "MCP".

penalty

Function to implement the soft-, MCP, SCAD thresholding rule in the ADMM method.

We explore sparsity and homogeneity of regression coefficients incorporating prior constraint information. A general pairwise fusion approach is proposed to deal with the sparsity and homogeneity detection when combining prior convex constraints. We develop an modified alternating direction method of multipliers algorithm (ADMM) to obtain the estimators.

thresh_est: Threshold estimation

Description

Usage

Arguments

Value

References