Abstract
Traditionally the most important problem of mathematical statistics dealing with random stationary processes Xt, t = …,-1,0,1, … is the problem of estimating the second order characteristics, namely the covariance function
or its Fourier transform — the spectral density f = f(λ) (under the assumption that the spectral density exists). For this reason, a vast amount of periodical and monographic literature is devoted to the nonparametric statistical problem of estimating the function β(τ) and especially that of f(λ) (see, for example, the books [4,21,22,26,56,77,137,139,140,]). However, the empirical value f *n of the spectral density f obtained by applying a certain statistical procedure to the observed values of the variables X1, … , Xn, usually depends in a complicated manner on the cyclic frequency λ. This fact often presents difficulties in applying the obtained estimate f *n of the function f to the solution of specific problems related to the process Xt.
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© 1986 Springer-Verlag New York Inc.
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Dzhaparidze, K. (1986). Introduction. In: Parameter Estimation and Hypothesis Testing in Spectral Analysis of Stationary Time Series. Springer Series in Statistics. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-4842-2_1
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DOI: https://doi.org/10.1007/978-1-4612-4842-2_1
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