Nonparametric log spectrum estimation using disconnected regression splines and genetic algorithms

This article proposes a new nonparametric procedure for estimating log spectra. This procedure consists of three major components: (1) a novel statistical model for modelling the unknown target log spectrum, (2) an AIC-based model selection criterion for choosing a ‘best’ 7tting model, and (3) a genetic algorithm for e8ectively searching the ‘best’ 7tting model. Numerical experiments are conducted to evaluate and compare the practical performance of the proposed procedure with some other common log spectral estimation procedures appearing in the literature. These other procedures include wavelet techniques, kernel smoothing and regression spline 7tting. Empirical results suggest that the proposed procedure compares favourably against all these procedures, especially when the unknown log spectrum contains inhomogeneous structures. ? 2002 Elsevier Science B.V. All rights reserved.