Empirical Process Techniques for Dependent Data:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Boston, MA
Birkhäuser Boston
2002
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Beschreibung: | Empirical process techniques for independent data have been used for many years in statistics and probability theory. These techniques have proved very useful for studying asymptotic properties of parametric as well as non-parametric statistical procedures. Recently, the need to model the dependence structure in data sets from many different subject areas such as finance, insurance, and telecommunications has led to new developments concerning the empirical distribution function and the empirical process for dependent, mostly stationary sequences. This work gives an introduction to this new theory of empirical process techniques, which has so far been scattered in the statistical and probabilistic literature, and surveys the most recent developments in various related fields. Key features: A thorough and comprehensive introduction to the existing theory of empirical process techniques for dependent data * Accessible surveys by leading experts of the most recent developments in various related fields * Examines empirical process techniques for dependent data, useful for studying parametric and non-parametric statistical procedures * Comprehensive bibliographies * An overview of applications in various fields related to empirical processes: e.g., spectral analysis of time-series, the bootstrap for stationary sequences, extreme value theory, and the empirical process for mixing dependent observations, including the case of strong dependence. To date this book is the only comprehensive treatment of the topic in book literature. It is an ideal introductory text that will serve as a reference or resource for classroom use in the areas of statistics, time-series analysis, extreme value theory, point process theory, and applied probability theory. Contributors: P. Ango Nze, M.A. Arcones, I. Berkes, R. Dahlhaus, J. Dedecker, H.G. Dehling |
| Beschreibung: | 1 Online-Ressource (XI, 383 p) |
| ISBN: | 9781461200994 9781461266112 |
| DOI: | 10.1007/978-1-4612-0099-4 |
Internformat
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Datensatz im Suchindex
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| spelling | Dehling, Herold Verfasser aut Empirical Process Techniques for Dependent Data edited by Herold Dehling, Thomas Mikosch, Michael Sørensen Boston, MA Birkhäuser Boston 2002 1 Online-Ressource (XI, 383 p) txt rdacontent c rdamedia cr rdacarrier Empirical process techniques for independent data have been used for many years in statistics and probability theory. These techniques have proved very useful for studying asymptotic properties of parametric as well as non-parametric statistical procedures. Recently, the need to model the dependence structure in data sets from many different subject areas such as finance, insurance, and telecommunications has led to new developments concerning the empirical distribution function and the empirical process for dependent, mostly stationary sequences. This work gives an introduction to this new theory of empirical process techniques, which has so far been scattered in the statistical and probabilistic literature, and surveys the most recent developments in various related fields. Key features: A thorough and comprehensive introduction to the existing theory of empirical process techniques for dependent data * Accessible surveys by leading experts of the most recent developments in various related fields * Examines empirical process techniques for dependent data, useful for studying parametric and non-parametric statistical procedures * Comprehensive bibliographies * An overview of applications in various fields related to empirical processes: e.g., spectral analysis of time-series, the bootstrap for stationary sequences, extreme value theory, and the empirical process for mixing dependent observations, including the case of strong dependence. To date this book is the only comprehensive treatment of the topic in book literature. It is an ideal introductory text that will serve as a reference or resource for classroom use in the areas of statistics, time-series analysis, extreme value theory, point process theory, and applied probability theory. Contributors: P. Ango Nze, M.A. Arcones, I. Berkes, R. Dahlhaus, J. Dedecker, H.G. Dehling Mathematics Distribution (Probability theory) Mathematical statistics Economics / Statistics Probability Theory and Stochastic Processes Statistics for Business/Economics/Mathematical Finance/Insurance Statistical Theory and Methods Mathematik Statistik Wirtschaft Empirischer Prozess (DE-588)4224810-3 gnd rswk-swf Empirischer Prozess (DE-588)4224810-3 s 1\p DE-604 Mikosch, Thomas Sonstige oth Sørensen, Michael Sonstige oth https://doi.org/10.1007/978-1-4612-0099-4 Verlag Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Dehling, Herold Empirical Process Techniques for Dependent Data Mathematics Distribution (Probability theory) Mathematical statistics Economics / Statistics Probability Theory and Stochastic Processes Statistics for Business/Economics/Mathematical Finance/Insurance Statistical Theory and Methods Mathematik Statistik Wirtschaft Empirischer Prozess (DE-588)4224810-3 gnd |
| subject_GND | (DE-588)4224810-3 |
| title | Empirical Process Techniques for Dependent Data |
| title_auth | Empirical Process Techniques for Dependent Data |
| title_exact_search | Empirical Process Techniques for Dependent Data |
| title_full | Empirical Process Techniques for Dependent Data edited by Herold Dehling, Thomas Mikosch, Michael Sørensen |
| title_fullStr | Empirical Process Techniques for Dependent Data edited by Herold Dehling, Thomas Mikosch, Michael Sørensen |
| title_full_unstemmed | Empirical Process Techniques for Dependent Data edited by Herold Dehling, Thomas Mikosch, Michael Sørensen |
| title_short | Empirical Process Techniques for Dependent Data |
| title_sort | empirical process techniques for dependent data |
| topic | Mathematics Distribution (Probability theory) Mathematical statistics Economics / Statistics Probability Theory and Stochastic Processes Statistics for Business/Economics/Mathematical Finance/Insurance Statistical Theory and Methods Mathematik Statistik Wirtschaft Empirischer Prozess (DE-588)4224810-3 gnd |
| topic_facet | Mathematics Distribution (Probability theory) Mathematical statistics Economics / Statistics Probability Theory and Stochastic Processes Statistics for Business/Economics/Mathematical Finance/Insurance Statistical Theory and Methods Mathematik Statistik Wirtschaft Empirischer Prozess |
| url | https://doi.org/10.1007/978-1-4612-0099-4 |
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