A bootstrap model selection criterion based on kullback's symmetric divergence
Abd‐Krim Seghouane, Lieven De Lathauwer · 2004
Following in the recent work of J. Cavanaugh (1999) and A.K. Seghouane (2002), a new corrected variant of KIC develop for the purpose of sources separation is proposed in this paper. The variant utilizes bootstrapping to provide an estimate of the expected Kullback-Leibler symmetric divergence between the model generating the data and a fitted approximating model. Simulation results that illustrate the performance of the new proposed criterion for the detection of the number of signals received by a sensor array are presented. As a result, the KIC variant serves as an effective tool for estimating the number of sources compared to other well known criteria.