Nonparametric log spectrum estimation using disconnected regression splines and genetic algorithms

Authors:
Thomas C. M. Lee;Tan F. Wong
Affiliations:
Department of Statistics, Colorado State University, Fort Collins, CO;Department of Electrical and Computer Engineering, University of Florida, 461 Engineering Building, P.O. Box 116130, Gainesville, FL
Venue:
Signal Processing
Year:
2003

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Abstract

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' fitting model, and (3) a genetic algorithm for effectively searching the 'best' fitting 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 fitting. Empirical results suggest that the proposed procedure compares favourably against all these procedures, especially when the unknown log spectrum contains inhomogeneous structures.