  Model selection is basically a process of finding the best model from the
subset of models in which the explanatory variables are effective on the
response variable. The log likelihood function for the lack of fit term and a
specified penalty term are used as two parts in a model selection criteria. In
this paper, we derive a new tool for the model selection in robust regression.
We introduce a new definition of relative entropy based on objective functions.
Due to the analytical simplicity, we use Huber's objective function $\rho_H$
and propose our specified penalty term $C_0^{\rho_H}$ to derive new Information
Complexity Criterion ($RICOMP_{C_0^{\rho_H}}$) as a robust model selection
tool. Additionally, by using the properties of $C_0^{\rho_H}$, we propose a new
value of tuning parameter called $k_{C_0}$ for the Huber's $\rho_H$. If a
contamination to normal distribution exists, $RICOMP_{C_0^{\rho_H}}$ chooses
the true model better than the rival ones. Monte Carlo Simulation studies are
carried out to show the utility both of $k_{C_0}$ and $RICOMP_{C_0^{\rho_H}}$.
A real data example is also given.
