Proposing robust LAD-Atan penalty of regression model estimation for high dimensional data

Abstract EN

The issue of penalized regression model has received considerable critical attention to variable selection.

It plays an essential role in dealing with high dimensional data.

Arctangent denoted by the Atan penalty has been used in both estimation and variable selection as an efficient method recently.

However, the Atan penalty is very sensitive to outliers in response to variables or heavy-tailed error distribution.

While the least absolute deviation is a good method to get robustness in regression estimation.

The specific objective of this research is to propose a robust Atan estimator from combining these two ideas at once.

Simulation experiments and real data applications show that the proposed LAD-Atan estimator has superior performance compared with other estimators.