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Can I read Nonparametric Log Spectrum Estimation Using Disconnected Regression Splines and Genetic Algorithms on EtoBox?
Nonparametric Log Spectrum Estimation Using Disconnected Regression Splines and Genetic Algorithms by Thomas C.M Lee; Tan F Wong is a Computer Science article available to read on EtoBox.
What is Nonparametric Log Spectrum Estimation Using Disconnected Regression Splines and Genetic Algorithms about?
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' ÿtting model, and (3) a genetic algorithm for e ectively searching the 'best' ÿtting 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 ÿtting. Empirical results suggest that the proposed procedure compares favourably against all these procedures, especially when the unknown log spectrum contains inhomogeneous structures.
Who reads Nonparametric Log Spectrum Estimation Using Disconnected Regression Splines and Genetic Algorithms?
It is typically read by researchers, students, and practitioners in Computer Science.
- Author
- Thomas C.M Lee; Tan F Wong
- Publisher
- Elsevier Science; Elsevier ; Elsevier BV (ISSN 0165-1684)
- Published
- 2003
- Language
- EN
- Field
- Computer Science (Physical Sciences)