9781441929655 - Predictions in Time Series Using Regression Models de Stulajter, Frantisek (12 resultados)

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Condición: New. pp. 244.

Paperback. Condición: Brand New. 240 pages. 9.25x6.10x0.55 inches. In Stock.

Taschenbuch. Condición: 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.

Taschenbuch. Condición: 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.

Condición: new. Questo è un articolo print on demand.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book will interest and assist people who are dealing with the problems of predictions of time series in higher education and research. It will greatly assist people who apply time series theory to practical problems in their work and also serve as a textbook for postgraduate students in statistics economics and related subjects. 244 pp. Englisch.

Condición: New. Print on Demand pp. 244 23:B&W 6 x 9 in or 229 x 152 mm Perfect Bound on White w/Gloss Lam.

Condición: New. PRINT ON DEMAND pp. 244.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. &nbsp-Appeals to finance analysts and econometricians&nbspThis book will interest and assist people who are dealing with the problems of predictions of time series in higher education and research. It will greatly assist people wh.

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. 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 KG, Sachsenplatz 4-6, 1201 Wien 244 pp. Englisch.