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Parameter Estimation and Hypothesis Testing in Spectral Analysis of Stationary Time Series (Springer Series in Statistics) - Softcover
Synopsis
. . ) (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
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- PublisherSpringer
- Publication date2011
- ISBN 10 1461293251
- ISBN 13 9781461293255
- BindingPaperback
- LanguageEnglish
- Number of pages324
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Parameter Estimation and Hypothesis Testing in Spectral Analysis of Stationary Time Series (Springer Series in Statistics)
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Parameter Estimation and Hypothesis Testing in Spectral Analysis of Stationary Time Series
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Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. . . ) (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, fo. Seller Inventory # 4191844
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Parameter Estimation and Hypothesis Testing in Spectral Analysis of Stationary Time Series
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - . . ) (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. Seller Inventory # 9781461293255
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