Robustness Measure Using 6 Dimensional Model with Empirical Distribution Approach

We propose the study of robustness measures for signal detection in noise in conjunction with an empirical distribution approach. We compare stationary to non-stationary noise in the form of three samples/six dimensions and our approach shows that robustness is barely reduced by admitting non-stationarity. In addition, this paper shows that robustness decreases with larger sample sizes, but there is a convergence in this decrease for sample sizes greater than 14.