Reducing the bias of multitaper spectrum estimates
Corresponding Author
G. A. Prieto
Scripps Institution of Oceanography, University of California San Diego, La Jolla CA 92093 , USA
Now at: Department of Geophysics, Stanford University, Stanford, CA 94305, USA. E-mail: [email protected].Search for more papers by this authorR. L. Parker
Scripps Institution of Oceanography, University of California San Diego, La Jolla CA 92093 , USA
Search for more papers by this authorD. J. Thomson
Mathematics and Statistics Department, Queens University, Kingston ON, Canada
Search for more papers by this authorF. L. Vernon
Scripps Institution of Oceanography, University of California San Diego, La Jolla CA 92093 , USA
Search for more papers by this authorR. L. Graham
Department of Computer Science and Engineering, University of California San Diego, La Jolla CA 92093 , USA
Search for more papers by this authorCorresponding Author
G. A. Prieto
Scripps Institution of Oceanography, University of California San Diego, La Jolla CA 92093 , USA
Now at: Department of Geophysics, Stanford University, Stanford, CA 94305, USA. E-mail: [email protected].Search for more papers by this authorR. L. Parker
Scripps Institution of Oceanography, University of California San Diego, La Jolla CA 92093 , USA
Search for more papers by this authorD. J. Thomson
Mathematics and Statistics Department, Queens University, Kingston ON, Canada
Search for more papers by this authorF. L. Vernon
Scripps Institution of Oceanography, University of California San Diego, La Jolla CA 92093 , USA
Search for more papers by this authorR. L. Graham
Department of Computer Science and Engineering, University of California San Diego, La Jolla CA 92093 , USA
Search for more papers by this authorSUMMARY
The power spectral density of geophysical signals provides information about the processes that generated them. We present a new approach to determine power spectra based on Thomson's multitaper analysis method. Our method reduces the bias due to the curvature of the spectrum close to the frequency of interest. Even while maintaining the same resolution bandwidth, bias is reduced in areas where the power spectrum is significantly quadratic. No additional sidelobe leakage is introduced. In addition, our methodology reliably estimates the derivatives (slope and curvature) of the spectrum. The extra information gleaned from the signal is useful for parameter estimation or to compare different signals.
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