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Titlebook: Conjugate Duality and the Exponential Fourier Spectrum; Wray Britton Book 1983 Springer-Verlag New York Inc. 1983 Duality.Dualität (Math.)

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书目名称Conjugate Duality and the Exponential Fourier Spectrum
编辑Wray Britton
视频video
丛书名称Lecture Notes in Statistics
图书封面Titlebook: Conjugate Duality and the Exponential Fourier Spectrum;  Wray Britton Book 1983 Springer-Verlag New York Inc. 1983 Duality.Dualität (Math.)
描述For some fields such as econometrics (Shore, 1980), oil prospecting (Claerbout, 1976), speech recognition (Levinson and Lieberman, 1981), satellite monitoring (Lavergnat et al., 1980), epilepsy diagnosis (Gersch and Tharp, 1977), and plasma physics (Bloomfield, 1976), there is a need to obtain an estimate of the spectral density (when it exists) in order to gain at least a crude understanding of the frequency content of time series data. An outstanding tutorial on the classical problem of spectral density estimation is given by Kay and Marple (1981). For an excellent collection of fundamental papers dealing with modern spec­ tral density estimation as well as an extensive bibliography on other fields of application, see Childers (1978). To devise a high-performance sample spectral density estimator, one must develop a rational basis for its construction, provide a feasible algorithm, and demonstrate its performance with respect to prescribed criteria. An algorithm is certainly feasible if it can be implemented on a computer, possesses computational efficiency (as measured by compu­ tational complexity analysis), and exhibits numerical stability. An estimator shows high performance
出版日期Book 1983
关键词Duality; Dualität (Math; ); Estimator; Excel; Harmonische Analyse; Sequentialanalyse; Stochastische Approxi
版次1
doihttps://doi.org/10.1007/978-1-4612-5528-4
isbn_softcover978-0-387-90826-7
isbn_ebook978-1-4612-5528-4Series ISSN 0930-0325 Series E-ISSN 2197-7186
issn_series 0930-0325
copyrightSpringer-Verlag New York Inc. 1983
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Conjugate Duality and the Exponential Fourier Spectrum
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Introduction,anding tutorial on the classical problem of spectral density estimation is given by Kay and Marple (1981). For an excellent collection of fundamental papers dealing with modern spectral density estimation as well as an extensive bibliography on other fields of application, see Childers (1978).
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The numerical experiment: results,nsformation y.: R → R defined by:.,.where . with . and s the sample mean and standard deviation of the given time series. Both compositional factors of y. are well known; in particular, the outer factor is the variance stabilizer of the Student t.
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