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This book addresses the problems of prediction of time series by using regression models on mean values and covariance functions. It will be of interest to researchers.
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Predictions in Time Series Using Regression Models
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Predictions in Time Series Using Regression Models
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Springer New York, Springer New York Mai 2011, 2011
ISBN 10: 1441929657
ISBN 13: 9781441929655
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Taschenbuch. Zustand: Neu. Neuware -Books on time series models deal mainly with models based on Box-Jenkins methodology which is generally represented by autoregressive integrated moving average models or some nonlinear extensions of these models, such as generalized autoregressive conditional heteroscedasticity models. Statistical inference for these models is well developed and commonly used in practical applications, due also to statistical packages containing time series analysis parts. The present book is based on regression models used for time series. These models are used not only for modeling mean values of observed time se ries, but also for modeling their covariance functions which are often given parametrically. Thus for a given finite length observation of a time series we can write the regression model in which the mean value vectors depend on regression parameters and the covariance matrices of the observation depend on variance-covariance parameters. Both these dependences can be linear or nonlinear. The aim of this book is to give an unified approach to the solution of statistical problems for such time series models, and mainly to problems of the estimation of unknown parameters of models and to problems of the prediction of time series modeled by regression models.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 244 pp. Englisch. Artikel-Nr. 9781441929655
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Predictions in Time Series Using Regression Models
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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Books on time series models deal mainly with models based on Box-Jenkins methodology which is generally represented by autoregressive integrated moving average models or some nonlinear extensions of these models, such as generalized autoregressive conditional heteroscedasticity models. Statistical inference for these models is well developed and commonly used in practical applications, due also to statistical packages containing time series analysis parts. The present book is based on regression models used for time series. These models are used not only for modeling mean values of observed time se ries, but also for modeling their covariance functions which are often given parametrically. Thus for a given finite length observation of a time series we can write the regression model in which the mean value vectors depend on regression parameters and the covariance matrices of the observation depend on variance-covariance parameters. Both these dependences can be linear or nonlinear. The aim of this book is to give an unified approach to the solution of statistical problems for such time series models, and mainly to problems of the estimation of unknown parameters of models and to problems of the prediction of time series modeled by regression models. Artikel-Nr. 9781441929655
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Predictions in Time Series Using Regression Models
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Taschenbuch. Zustand: Neu. Predictions in Time Series Using Regression Models | Frantisek Stulajter | Taschenbuch | ix | Englisch | 2011 | Springer | EAN 9781441929655 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. Artikel-Nr. 107252975
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