nonlinear time series analysis de tsay ruey (19 resultados)

Condición: As New. Unread book in perfect condition.

Condición: As New. Unread book in perfect condition.

Hardback. Condición: New. New copy - Usually dispatched within 4 working days. 726.

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Condición: New.

Condición: New.

Condición: New.

Hardcover. Condición: new. Hardcover. A comprehensive resource that draws a balance between theory and applications of nonlinear time series analysis Nonlinear Time Series Analysis offers an important guide to both parametric and nonparametric methods, nonlinear state-space models, and Bayesian as well as classical approaches to nonlinear time series analysis. The authorsnoted experts in the fieldexplore the advantages and limitations of the nonlinear models and methods and review the improvements upon linear time series models. The need for this book is based on the recent developments in nonlinear time series analysis, statistical learning, dynamic systems and advanced computational methods. Parametric and nonparametric methods and nonlinear and non-Gaussian state space models provide a much wider range of tools for time series analysis. In addition, advances in computing and data collection have made available large data sets and high-frequency data. These new data make it not only feasible, but also necessary to take into consideration the nonlinearity embedded in most real-world time series. This vital guide: Offers research developed by leading scholars of time series analysis Presents R commands making it possible to reproduce all the analyses included in the text Contains real-world examples throughout the book Recommends exercises to test understanding of material presented Includes an instructor solutions manual and companion website Written for students, researchers, and practitioners who are interested in exploring nonlinearity in time series, Nonlinear Time Series Analysis offers a comprehensive text that explores the advantages and limitations of the nonlinear models and methods and demonstrates the improvements upon linear time series models. A comprehensive resource that draws a balance between theory and applications of nonlinear time series analysis Nonlinear Time Series Analysis offers an important guide to both parametric and nonparametric methods, nonlinear state-space models, and Bayesian as well as classical approaches to nonlinear time series analysis. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Condición: New. &Uumlber den AutorRUEY S. TSAY, PHD, is H.G.B. Alexander Professor of Econometrics and Statistics at The University of Chicago Booth School of Business. He is a fellow of the American Statistical Association and the Institute.

Condición: New.

Condición: New. 2018. 1st Edition. Hardcover. . . . . .

Hardcover. Condición: new. Hardcover. A comprehensive resource that draws a balance between theory and applications of nonlinear time series analysis Nonlinear Time Series Analysis offers an important guide to both parametric and nonparametric methods, nonlinear state-space models, and Bayesian as well as classical approaches to nonlinear time series analysis. The authorsnoted experts in the fieldexplore the advantages and limitations of the nonlinear models and methods and review the improvements upon linear time series models. The need for this book is based on the recent developments in nonlinear time series analysis, statistical learning, dynamic systems and advanced computational methods. Parametric and nonparametric methods and nonlinear and non-Gaussian state space models provide a much wider range of tools for time series analysis. In addition, advances in computing and data collection have made available large data sets and high-frequency data. These new data make it not only feasible, but also necessary to take into consideration the nonlinearity embedded in most real-world time series. This vital guide: Offers research developed by leading scholars of time series analysis Presents R commands making it possible to reproduce all the analyses included in the text Contains real-world examples throughout the book Recommends exercises to test understanding of material presented Includes an instructor solutions manual and companion website Written for students, researchers, and practitioners who are interested in exploring nonlinearity in time series, Nonlinear Time Series Analysis offers a comprehensive text that explores the advantages and limitations of the nonlinear models and methods and demonstrates the improvements upon linear time series models. A comprehensive resource that draws a balance between theory and applications of nonlinear time series analysis Nonlinear Time Series Analysis offers an important guide to both parametric and nonparametric methods, nonlinear state-space models, and Bayesian as well as classical approaches to nonlinear time series analysis. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Hardcover. Condición: Brand New. 496 pages. 9.00x6.00x1.00 inches. In Stock.

Condición: New. 2018. 1st Edition. Hardcover. . . . . . Books ship from the US and Ireland.

Hardcover. Condición: new. Hardcover. A comprehensive resource that draws a balance between theory and applications of nonlinear time series analysis Nonlinear Time Series Analysis offers an important guide to both parametric and nonparametric methods, nonlinear state-space models, and Bayesian as well as classical approaches to nonlinear time series analysis. The authorsnoted experts in the fieldexplore the advantages and limitations of the nonlinear models and methods and review the improvements upon linear time series models. The need for this book is based on the recent developments in nonlinear time series analysis, statistical learning, dynamic systems and advanced computational methods. Parametric and nonparametric methods and nonlinear and non-Gaussian state space models provide a much wider range of tools for time series analysis. In addition, advances in computing and data collection have made available large data sets and high-frequency data. These new data make it not only feasible, but also necessary to take into consideration the nonlinearity embedded in most real-world time series. This vital guide: Offers research developed by leading scholars of time series analysis Presents R commands making it possible to reproduce all the analyses included in the text Contains real-world examples throughout the book Recommends exercises to test understanding of material presented Includes an instructor solutions manual and companion website Written for students, researchers, and practitioners who are interested in exploring nonlinearity in time series, Nonlinear Time Series Analysis offers a comprehensive text that explores the advantages and limitations of the nonlinear models and methods and demonstrates the improvements upon linear time series models. A comprehensive resource that draws a balance between theory and applications of nonlinear time series analysis Nonlinear Time Series Analysis offers an important guide to both parametric and nonparametric methods, nonlinear state-space models, and Bayesian as well as classical approaches to nonlinear time series analysis. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Hardback. Condición: New. This item is printed on demand. New copy - Usually dispatched within 5-9 working days 726.

Hardcover. Condición: Brand New. 496 pages. 9.00x6.00x1.00 inches. In Stock. This item is printed on demand.