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Model selection is basically a process of finding the best model from thesubset of models in which the explanatory variables are effective on theresponse variable. The log likelihood function for the lack of fit term and aspecified penalty term are used as two parts in a model selection criteria. Inthis 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 InformationComplexity Criterion ($RICOMP_{C_0^{\rho_H}}$) as a robust model selectiontool. Additionally, by using the properties of $C_0^{\rho_H}$, we propose a newvalue of tuning parameter called $k_{C_0}$ for the Huber's $\rho_H$. If acontamination to normal distribution exists, $RICOMP_{C_0^{\rho_H}}$ choosesthe true model better than the rival ones. Monte Carlo Simulation studies arecarried out to show the utility both of $k_{C_0}$ and $RICOMP_{C_0^{\rho_H}}$.A real data example is also given.

Information Complexity Criterion for Model Selection in Robust Regression Using A New Robust Penalty Term