Stochastic Complexity for the Estimation of Sine-Waves in Colored Noise

During recent years the advances in stochastic complexity (SC) have led to new exact formulae or to sharper approximations for large classes of models. We focus on the use of the SC to estimate the structure for the model of sine-waves in Gaussian autoregressive noise. Since the evaluation of SC relies on the determinant of the Fisher information matrix (FEM), the computation of FIM is revisited. It is shown for small and moderate sample sizes that SC compares favorably with other well-known criteria such as: BIC, KICc and GAIC.