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Robust and Nonlinear Time Series Analysis: Proceedings of a Workshop Organized by the Sonderforschungsbereich 123 ''Stochastische Mathematische ... 1983: 26 (Lecture Notes in Statistics) - Softcover
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Classical time series methods are based on the assumption that a particular stochastic process model generates the observed data. The, most commonly used assumption is that the data is a realization of a stationary Gaussian process. However, since the Gaussian assumption is a fairly stringent one, this assumption is frequently replaced by the weaker assumption that the process is wide~sense stationary and that only the mean and covariance sequence is specified. This approach of specifying the probabilistic behavior only up to "second order" has of course been extremely popular from a theoretical point of view be cause it has allowed one to treat a large variety of problems, such as prediction, filtering and smoothing, using the geometry of Hilbert spaces. While the literature abounds with a variety of optimal estimation results based on either the Gaussian assumption or the specification of second-order properties, time series workers have not always believed in the literal truth of either the Gaussian or second-order specifica tion. They have none-the-less stressed the importance of such optimali ty results, probably for two main reasons: First, the results come from a rich and very workable theory. Second, the researchers often relied on a vague belief in a kind of continuity principle according to which the results of time series inference would change only a small amount if the actual model deviated only a small amount from the assum ed model.
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Classical time series methods are based on the assumption that a particular stochastic process model generates the observed data. The, most commonly used assumption is that the data is a realization of a stationary Gaussian process. However, since the Gaussian assumption is a fairly stringent one, this assumption is frequently replaced by the weaker assumption that the process is wide~sense stationary and that only the mean and covariance sequence is specified. This approach of specifying the probabilistic behavior only up to "second order" has of course been extremely popular from a theoretical point of view be cause it has allowed one to treat a large variety of problems, such as prediction, filtering and smoothing, using the geometry of Hilbert spaces. While the literature abounds with a variety of optimal estimation results based on either the Gaussian assumption or the specification of second-order properties, time series workers have not always believed in the literal truth of either the Gaussian or second-order specifica tion. They have none-the-less stressed the importance of such optimali ty results, probably for two main reasons: First, the results come from a rich and very workable theory. Second, the researchers often relied on a vague belief in a kind of continuity principle according to which the results of time series inference would change only a small amount if the actual model deviated only a small amount from the assum ed model.
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Robust and nonlinear time series analysis Proceedings of a workshop Heidelberg 1983
Verlag:
Springer, New York ; Berlin ; Heidelberg ; Tokyo, 1984
ISBN 10: 038796102X
ISBN 13: 9780387961026
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Kartoniert. Zustand: Gut. 25 cm Lecture Notes in Statistics, 26. VII, 286 S. Orig.-Karton. Mit graphischen Darstellungen. Gutes Exemplar. Bestandsnummer des Verkäufers 31035
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Robust and Nonlinear Time Series Analysis: Proceedings of a Workshop Organized
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Zustand: Used: Good. former library 1984 paperback vol 26 withdrawn stamp in book/ on edge of pages clean text tanned pages 286 pages/// K-13. Bestandsnummer des Verkäufers 0129N0FBC8V
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Robust and Nonlinear Time Series Analysis Proceedings of a Workshop Organized by the Sonderforschungsbereich 123 "Stochastische Mathematische Modelle", Heidelberg 1983
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Robust and Nonlinear Time Series Analysis
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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Classical time series methods are based on the assumption that a particular stochastic process model generates the observed data. The, most commonly used assumption is that the data is a realization of a stationary Gaussian process. However, since the Gauss. Bestandsnummer des Verkäufers 5912662
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Robust and Nonlinear Time Series Analysis
Verlag:
Springer New York, Springer New York Dez 1984, 1984
ISBN 10: 038796102X
ISBN 13: 9780387961026
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Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Classical time series methods are based on the assumption that a particular stochastic process model generates the observed data. The, most commonly used assumption is that the data is a realization of a stationary Gaussian process. However, since the Gaussian assumption is a fairly stringent one, this assumption is frequently replaced by the weaker assumption that the process is wide~sense stationary and that only the mean and covariance sequence is specified. This approach of specifying the probabilistic behavior only up to 'second order' has of course been extremely popular from a theoretical point of view be cause it has allowed one to treat a large variety of problems, such as prediction, filtering and smoothing, using the geometry of Hilbert spaces. While the literature abounds with a variety of optimal estimation results based on either the Gaussian assumption or the specification of second-order properties, time series workers have not always believed in the literal truth of either the Gaussian or second-order specifica tion. They have none-the-less stressed the importance of such optimali ty results, probably for two main reasons: First, the results come from a rich and very workable theory. Second, the researchers often relied on a vague belief in a kind of continuity principle according to which the results of time series inference would change only a small amount if the actual model deviated only a small amount from the assum ed model.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 300 pp. Englisch. Bestandsnummer des Verkäufers 9780387961026
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Robust and Nonlinear Time Series Analysis
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Classical time series methods are based on the assumption that a particular stochastic process model generates the observed data. The, most commonly used assumption is that the data is a realization of a stationary Gaussian process. However, since the Gaussian assumption is a fairly stringent one, this assumption is frequently replaced by the weaker assumption that the process is wide~sense stationary and that only the mean and covariance sequence is specified. This approach of specifying the probabilistic behavior only up to 'second order' has of course been extremely popular from a theoretical point of view be cause it has allowed one to treat a large variety of problems, such as prediction, filtering and smoothing, using the geometry of Hilbert spaces. While the literature abounds with a variety of optimal estimation results based on either the Gaussian assumption or the specification of second-order properties, time series workers have not always believed in the literal truth of either the Gaussian or second-order specifica tion. They have none-the-less stressed the importance of such optimali ty results, probably for two main reasons: First, the results come from a rich and very workable theory. Second, the researchers often relied on a vague belief in a kind of continuity principle according to which the results of time series inference would change only a small amount if the actual model deviated only a small amount from the assum ed model. 300 pp. Englisch. Bestandsnummer des Verkäufers 9780387961026
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Robust and Nonlinear Time Series Analysis : Proceedings of a Workshop Organized by the Sonderforschungsbereich 123 "Stochastische Mathematische Modelle", Heidelberg 1983
Verlag:
Springer New York, Springer New York, 1984
ISBN 10: 038796102X
ISBN 13: 9780387961026
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Classical time series methods are based on the assumption that a particular stochastic process model generates the observed data. The, most commonly used assumption is that the data is a realization of a stationary Gaussian process. However, since the Gaussian assumption is a fairly stringent one, this assumption is frequently replaced by the weaker assumption that the process is wide~sense stationary and that only the mean and covariance sequence is specified. This approach of specifying the probabilistic behavior only up to 'second order' has of course been extremely popular from a theoretical point of view be cause it has allowed one to treat a large variety of problems, such as prediction, filtering and smoothing, using the geometry of Hilbert spaces. While the literature abounds with a variety of optimal estimation results based on either the Gaussian assumption or the specification of second-order properties, time series workers have not always believed in the literal truth of either the Gaussian or second-order specifica tion. They have none-the-less stressed the importance of such optimali ty results, probably for two main reasons: First, the results come from a rich and very workable theory. Second, the researchers often relied on a vague belief in a kind of continuity principle according to which the results of time series inference would change only a small amount if the actual model deviated only a small amount from the assum ed model. Bestandsnummer des Verkäufers 9780387961026
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Robust and Nonlinear Time Series Analysis: Proceedings of a Workshop Organized by the Sonderforschungsbereich 123 "Stochastische Mathematische ... 1983 (Lecture Notes in Statistics, 26)
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Robust and Nonlinear Time Series Analysis: Proceedings of a Workshop Organized by the Sonderforschungsbereich 123 "Stochastische Mathematische ... 1983 (Lecture Notes in Statistics, 26)
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Robust and Nonlinear Time Series Analysis: Proceedings of a Workshop Organized by the Sonderforschungsbereich 123 "Stochastische Mathematische Modelle", Heidelberg 1983
Verlag:
Springer-Verlag New York Inc., 1984
ISBN 10: 038796102X
ISBN 13: 9780387961026
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