robust nonlinear time series (26 risultati)

Softcover. VII, 286 S. Ehem. Bibliotheksexemplar mit Signatur und Stempel. GUTER Zustand, ein paar Gebrauchsspuren. Ex-library with stamp and library-signature. GOOD condition, some traces of use. X-16546 354096102X Sprache: Englisch Gewicht in Gramm: 490.

Kartoniert. Condizione: Gut. 25 cm Lecture Notes in Statistics, 26. VII, 286 S. Orig.-Karton. Mit graphischen Darstellungen. Gutes Exemplar.

Paperback. Condizione: Fair. Paper browned, otherwise text clean and solid; Lecture Notes in Statistics; 9.61 X 6.69 X 0.68 inches; 286 pages.

Condizione: Used: Good. former library 1984 paperback vol 26 withdrawn stamp in book/ on edge of pages clean text tanned pages 286 pages/// K-13.

Broschiert. Condizione: Gut. 286 Seiten Das hier angebotene Buch stammt aus einer teilaufgelösten Bibliothek und kann die entsprechenden Kennzeichnungen aufweisen (Rückenschild, Instituts-Stempel.); der Buchzustand ist ansonsten ordentlich und dem Alter entsprechend gut. In ENGLISCHER Sprache. Sprache: Deutsch Gewicht in Gramm: 460.

Condizione: Good. some extremities to the book with foxing to the outer edge of pages with a inscription to the intro page but does not affect the content.

Condizione: New.

Condizione: very good. Gut/Very good: Buch bzw. Schutzumschlag mit wenigen Gebrauchsspuren an Einband, Schutzumschlag oder Seiten. / Describes a book or dust jacket that does show some signs of wear on either the binding, dust jacket or pages.

Taschenbuch. Condizione: Neu. Neuware -Time series modeling and analysis is central to most financial and econometric data modeling. With increased globalization in trade, commerce and finance, national variables like gross domestic productivity (GDP) and unemployment rate, market variables like indices and stock prices and global variables like commodity prices are more tightly coupled than ever before. This translates to the use of multivariate or vector time series models and algorithms in analyzing and understanding the relationships that these variables share with each other. While robustness and time series modeling have been vastly researched individually in the past, application of robust methods to estimate time series models is still quite open. The central goal of this thesis is the study of the S-estimator, a robust estimator, applied to some simple vector and nonlinear time series models. In each case, we will look at the important aspect of stationarity of the model and analyze the asymptotic behavior of the S-estimator.Books on Demand GmbH, Überseering 33, 22297 Hamburg 156 pp. Englisch.

Condizione: New. In.

Taschenbuch. Condizione: Neu. Robust multivariate and nonlinear time series models | Application of robust estimators for the vector autoregressive and bilinear time series models | Ravi Ramakrishnan | Taschenbuch | 156 S. | Englisch | 2010 | LAP LAMBERT Academic Publishing | EAN 9783843357814 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

Condizione: New. pp. 300 1st Edition.

Paperback. Condizione: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Paperback. Condizione: Brand New. 1st edition. 286 pages. 9.75x6.75x0.75 inches. In Stock.

Taschenbuch. Condizione: Neu. Robust and Nonlinear Time Series Analysis | Proceedings of a Workshop Organized by the Sonderforschungsbereich 123 "Stochastische Mathematische Modelle", Heidelberg 1983 | J. Franke (u. a.) | Taschenbuch | 286 S. | Englisch | 1984 | Springer | EAN 9780387961026 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.

Taschenbuch. Condizione: 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.

Paperback. Condizione: Very Good. Very Good. book.

Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Time series modeling and analysis is central to most financial and econometric data modeling. With increased globalization in trade, commerce and finance, national variables like gross domestic productivity (GDP) and unemployment rate, market variables like indices and stock prices and global variables like commodity prices are more tightly coupled than ever before. This translates to the use of multivariate or vector time series models and algorithms in analyzing and understanding the relationships that these variables share with each other. While robustness and time series modeling have been vastly researched individually in the past, application of robust methods to estimate time series models is still quite open. The central goal of this thesis is the study of the S-estimator, a robust estimator, applied to some simple vector and nonlinear time series models. In each case, we will look at the important aspect of stationarity of the model and analyze the asymptotic behavior of the S-estimator. 156 pp. Englisch.

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Ramakrishnan RaviDr. Ravi Ramakrishnan completed his Ph.D in Mathematics from the Swiss Federal Institute of Technology, Lausanne (EPFL), Switzerland. Presently, he works with Banque Cantonale Vaudoise (BCV), Lausanne, Switzerland, a.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Time series modeling and analysis is central to most financial and econometric data modeling. With increased globalization in trade, commerce and finance, national variables like gross domestic productivity (GDP) and unemployment rate, market variables like indices and stock prices and global variables like commodity prices are more tightly coupled than ever before. This translates to the use of multivariate or vector time series models and algorithms in analyzing and understanding the relationships that these variables share with each other. While robustness and time series modeling have been vastly researched individually in the past, application of robust methods to estimate time series models is still quite open. The central goal of this thesis is the study of the S-estimator, a robust estimator, applied to some simple vector and nonlinear time series models. In each case, we will look at the important aspect of stationarity of the model and analyze the asymptotic behavior of the S-estimator.

Taschenbuch. Condizione: 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.

Condizione: 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.

Paperback / softback. Condizione: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 512.

Condizione: New. Print on Demand pp. 300 67:B&W 6.69 x 9.61 in or 244 x 170 mm (Pinched Crown) Perfect Bound on White w/Gloss Lam.

Condizione: New. PRINT ON DEMAND pp. 300.

Taschenbuch. Condizione: 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.