The books strength lies in its applicability to the needs of readers from many disciplines with varying backgrounds in mathematics. It provides a solid foundation in spectral analysis for fields that include statistics, signal process engineering, economics, geophysics, physics, and geology. Appendices provide details and proofs for those who are advanced in math. Theories are followed by examples and applications over a wide range of topics such as meteorology, seismology, and telecommunications.
Topics covered include Hilbert spaces; univariate models for spectral analysis; multivariate spectral models; sampling, aliasing, and discrete-time models; real-time filtering; digital filters; linear filters; distribution theory; sampling properties ofspectral estimates; and linear prediction.
- Hilbert spaces
- univariate models for spectral analysis
- multivariate spectral models
- sampling, aliasing, and discrete-time models
- real-time filtering
- digital filters
- linear filters
- distribution theory
- sampling properties of spectral estimates
- linear prediction
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