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Sinopsis
. . ) (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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. . ) (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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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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Condición: 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. Nº de ref. del artículo: 4191844
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Parameter Estimation and Hypothesis Testing in Spectral Analysis of Stationary Time Series
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Taschenbuch. Condición: 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. Nº de ref. del artículo: 9781461293255
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Parameter Estimation and Hypothesis Testing in Spectral Analysis of Stationary Time Series
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Parameter Estimation and Hypothesis Testing in Spectral Analysis of Stationary Time Series
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Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -. . ) (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 = 1Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 332 pp. Englisch. Nº de ref. del artículo: 9781461293255
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Parameter Estimation and Hypothesis Testing in Spectral Analysis of Stationary Time Series
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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 (Springer Series in Statistics)
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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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Springer New York Sep 2011, 2011
ISBN 10: 1461293251
ISBN 13: 9781461293255
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -. . ) (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 = 1 332 pp. Englisch. Nº de ref. del artículo: 9781461293255
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