Asymptotic Theory of Statistical Inference for Time Series:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
New York, NY
Springer New York
2000
|
| Schriftenreihe: | Springer Series in Statistics
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Beschreibung: | There has been much demand for the statistical analysis of dependent observations in many fields, for example, economics, engineering and the natural sciences. A model that describes the probability structure of a series of dependent observations is called a stochastic process. The primary aim of this book is to provide modern statistical techniques and theory for stochastic processes. The stochastic processes mentioned here are not restricted to the usual autoregressive (AR), moving average (MA), and autoregressive moving average (ARMA) processes. We deal with a wide variety of stochastic processes, for example, non-Gaussian linear processes, long-memory processes, nonlinear processes, orthogonal increment processes, and continuous time processes. For them we develop not only the usual estimation and testing theory but also many other statistical methods and techniques, such as discriminant analysis, cluster analysis, nonparametric methods, higher order asymptotic theory in view of differential geometry, large deviation principle, and saddlepoint approximation. Because it is difficult to use the exact distribution theory, the discussion is based on the asymptotic theory. Optimality of various procedures is often shown by use of local asymptotic normality (LAN), which is due to LeCam. This book is suitable as a professional reference book on statistical analysis of stochastic processes or as a textbook for students who specialize in statistics. It will also be useful to researchers, including those in econometrics, mathematics, and seismology, who utilize statistical methods for stochastic processes |
| Beschreibung: | 1 Online-Ressource (XVII, 662 p) |
| ISBN: | 9781461211624 9781461270287 |
| ISSN: | 0172-7397 |
| DOI: | 10.1007/978-1-4612-1162-4 |
Internformat
MARC
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| 500 | |a There has been much demand for the statistical analysis of dependent observations in many fields, for example, economics, engineering and the natural sciences. A model that describes the probability structure of a series of dependent observations is called a stochastic process. The primary aim of this book is to provide modern statistical techniques and theory for stochastic processes. The stochastic processes mentioned here are not restricted to the usual autoregressive (AR), moving average (MA), and autoregressive moving average (ARMA) processes. We deal with a wide variety of stochastic processes, for example, non-Gaussian linear processes, long-memory processes, nonlinear processes, orthogonal increment processes, and continuous time processes. For them we develop not only the usual estimation and testing theory but also many other statistical methods and techniques, such as discriminant analysis, cluster analysis, nonparametric methods, higher order asymptotic theory in view of differential geometry, large deviation principle, and saddlepoint approximation. Because it is difficult to use the exact distribution theory, the discussion is based on the asymptotic theory. Optimality of various procedures is often shown by use of local asymptotic normality (LAN), which is due to LeCam. This book is suitable as a professional reference book on statistical analysis of stochastic processes or as a textbook for students who specialize in statistics. It will also be useful to researchers, including those in econometrics, mathematics, and seismology, who utilize statistical methods for stochastic processes | ||
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Datensatz im Suchindex
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| discipline | Mathematik |
| doi_str_mv | 10.1007/978-1-4612-1162-4 |
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| isbn | 9781461211624 9781461270287 |
| issn | 0172-7397 |
| language | English |
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| spelling | Taniguchi, Masanobu 1951- Verfasser (DE-588)1089668007 aut Asymptotic Theory of Statistical Inference for Time Series by Masanobu Taniguchi, Yoshihide Kakizawa New York, NY Springer New York 2000 1 Online-Ressource (XVII, 662 p) txt rdacontent c rdamedia cr rdacarrier Springer Series in Statistics 0172-7397 There has been much demand for the statistical analysis of dependent observations in many fields, for example, economics, engineering and the natural sciences. A model that describes the probability structure of a series of dependent observations is called a stochastic process. The primary aim of this book is to provide modern statistical techniques and theory for stochastic processes. The stochastic processes mentioned here are not restricted to the usual autoregressive (AR), moving average (MA), and autoregressive moving average (ARMA) processes. We deal with a wide variety of stochastic processes, for example, non-Gaussian linear processes, long-memory processes, nonlinear processes, orthogonal increment processes, and continuous time processes. For them we develop not only the usual estimation and testing theory but also many other statistical methods and techniques, such as discriminant analysis, cluster analysis, nonparametric methods, higher order asymptotic theory in view of differential geometry, large deviation principle, and saddlepoint approximation. Because it is difficult to use the exact distribution theory, the discussion is based on the asymptotic theory. Optimality of various procedures is often shown by use of local asymptotic normality (LAN), which is due to LeCam. This book is suitable as a professional reference book on statistical analysis of stochastic processes or as a textbook for students who specialize in statistics. It will also be useful to researchers, including those in econometrics, mathematics, and seismology, who utilize statistical methods for stochastic processes Statistics Distribution (Probability theory) Mathematical statistics Statistical Theory and Methods Probability Theory and Stochastic Processes Statistik Asymptotische Statistik (DE-588)4203167-9 gnd rswk-swf Statistische Schlussweise (DE-588)4182963-3 gnd rswk-swf Stochastischer Prozess (DE-588)4057630-9 gnd rswk-swf Zeitreihenanalyse (DE-588)4067486-1 gnd rswk-swf Zeitreihenanalyse (DE-588)4067486-1 s Stochastischer Prozess (DE-588)4057630-9 s Statistische Schlussweise (DE-588)4182963-3 s Asymptotische Statistik (DE-588)4203167-9 s 1\p DE-604 Kakizawa, Yoshihide Sonstige oth https://doi.org/10.1007/978-1-4612-1162-4 Verlag Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Taniguchi, Masanobu 1951- Asymptotic Theory of Statistical Inference for Time Series Statistics Distribution (Probability theory) Mathematical statistics Statistical Theory and Methods Probability Theory and Stochastic Processes Statistik Asymptotische Statistik (DE-588)4203167-9 gnd Statistische Schlussweise (DE-588)4182963-3 gnd Stochastischer Prozess (DE-588)4057630-9 gnd Zeitreihenanalyse (DE-588)4067486-1 gnd |
| subject_GND | (DE-588)4203167-9 (DE-588)4182963-3 (DE-588)4057630-9 (DE-588)4067486-1 |
| title | Asymptotic Theory of Statistical Inference for Time Series |
| title_auth | Asymptotic Theory of Statistical Inference for Time Series |
| title_exact_search | Asymptotic Theory of Statistical Inference for Time Series |
| title_full | Asymptotic Theory of Statistical Inference for Time Series by Masanobu Taniguchi, Yoshihide Kakizawa |
| title_fullStr | Asymptotic Theory of Statistical Inference for Time Series by Masanobu Taniguchi, Yoshihide Kakizawa |
| title_full_unstemmed | Asymptotic Theory of Statistical Inference for Time Series by Masanobu Taniguchi, Yoshihide Kakizawa |
| title_short | Asymptotic Theory of Statistical Inference for Time Series |
| title_sort | asymptotic theory of statistical inference for time series |
| topic | Statistics Distribution (Probability theory) Mathematical statistics Statistical Theory and Methods Probability Theory and Stochastic Processes Statistik Asymptotische Statistik (DE-588)4203167-9 gnd Statistische Schlussweise (DE-588)4182963-3 gnd Stochastischer Prozess (DE-588)4057630-9 gnd Zeitreihenanalyse (DE-588)4067486-1 gnd |
| topic_facet | Statistics Distribution (Probability theory) Mathematical statistics Statistical Theory and Methods Probability Theory and Stochastic Processes Statistik Asymptotische Statistik Statistische Schlussweise Stochastischer Prozess Zeitreihenanalyse |
| url | https://doi.org/10.1007/978-1-4612-1162-4 |
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