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Añadir al carritoGebunden. Condición: New.  -Appeals to finance analysts and econometricians 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 wh.
Publicado por Springer New York, Springer New York Mai 2011, 2011
ISBN 10: 1441929657 ISBN 13: 9781441929655
Idioma: Inglés
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Añadir al carritoTaschenbuch. Condición: 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.
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Añadir al carritoTaschenbuch. 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.
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Añadir al carritoTaschenbuch. Condición: Neu. Predictions in Time Series Using Regression Models | Frantisek Stulajter | Taschenbuch | ix | Englisch | 2011 | Springer New York | EAN 9781441929655 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Añadir al carritoCondición: Sehr gut. Zustand: Sehr gut | Sprache: Englisch | Produktart: Bücher.
Publicado por Springer New York, 2002
Idioma: Inglés
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Añadir al carritoHardcover/ Pappband. Condición: Wie neu. 231 S. Economic Statistics Wirtschaftsstatistik Statistik Ökonometrie Econometrics Economy Wirtschaftswissenschaft Business Studies Mathematik Mathematics Sehr guter Zustand/ very good Ex-Library. ha1079774 Sprache: Englisch Gewicht in Gramm: 510.
Publicado por Springer New York Apr 2002, 2002
ISBN 10: 0387953507 ISBN 13: 9780387953502
Idioma: Inglés
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Añadir al carritoBuch. Condición: 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.