Parameter Estimation and Hypothesis Testing in Spectral Analysis of Stationary Time Series

  • K. Dzhaparidze

Part of the Springer Series in Statistics book series (SSS)

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About this book


. . ) (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 tJ( T) and especially that of leA) (see, for example, the books [4,21,22,26,56,77,137,139,140,]). However, the empirical value t;; of the spectral density I obtained by applying a certain statistical procedure to the observed values of the variables Xl' . . . , X , usually depends in n a complicated manner on the cyclic frequency). . This fact often presents difficulties in applying the obtained estimate t;; of the function I to the solution of specific problems rela ted to the process X . Theref ore, in practice, the t obtained values of the estimator t;; (or an estimator of the covariance function tJ~( T» are almost always "smoothed," i. e. , are approximated by values of a certain sufficiently simple function 1 = 1


Analysis Estimator Gaussian distribution Likelihood Series Time Time series best fit

Authors and affiliations

  • K. Dzhaparidze
    • 1
  1. 1.Mathematisch CentrumAmsterdamThe Netherlands

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag New York 1986
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4612-9325-5
  • Online ISBN 978-1-4612-4842-2
  • Series Print ISSN 0172-7397
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