Empirical Estimates in Stochastic Optimization and Identification:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Boston, MA
Springer US
2002
|
| Schriftenreihe: | Applied Optimization
71 |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Beschreibung: | One of the basic problems of statistical investigation is taking the best in some sense decision by observations of some totality of data. In this book empirical methods for solving of stochastic optimization problems and identification methods closely connected with them are investigated. The main attention is paid to studying of asymptotic behavior of the estimates, proving of the assertions about tending of the considered estimates to optimal ones under unlimited increase of the sample size. The sufficiently complete idea on empirical methods in the theory of optimization and estimation can be found in the monographs of Ibragimov and Has'minskii [52], Ermoliev and Wets [105], van de Geer [30], Pfanzagl and Wefelmeyer [107] and many others where the new approach to the investigation for discrete and continuous observations is considered. In the present work some new parametric problems of stochastic optimization and estimation are investigated, the sufficient attention is paid to nonparametric problems and continuous models with a multidimensional argument. The first chapter is auxiliary. The second one is devoted to investigation of empirical estimates in stochastic optimization problems. In the third chapter parametric regression models are considered, the connection between some of these problems and stochastic optimization problems being studied in the previous chapter is indicated. The fourth chapter is devoted to studying of so-called periodogram estimates |
| Beschreibung: | 1 Online-Ressource (VIII, 250 p) |
| ISBN: | 9781475735673 9781441952240 |
| ISSN: | 1384-6485 |
| DOI: | 10.1007/978-1-4757-3567-3 |
Internformat
MARC
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| 245 | 1 | 0 | |a Empirical Estimates in Stochastic Optimization and Identification |c by Pavel S. Knopov, Evgeniya J. Kasitskaya |
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| 500 | |a One of the basic problems of statistical investigation is taking the best in some sense decision by observations of some totality of data. In this book empirical methods for solving of stochastic optimization problems and identification methods closely connected with them are investigated. The main attention is paid to studying of asymptotic behavior of the estimates, proving of the assertions about tending of the considered estimates to optimal ones under unlimited increase of the sample size. The sufficiently complete idea on empirical methods in the theory of optimization and estimation can be found in the monographs of Ibragimov and Has'minskii [52], Ermoliev and Wets [105], van de Geer [30], Pfanzagl and Wefelmeyer [107] and many others where the new approach to the investigation for discrete and continuous observations is considered. In the present work some new parametric problems of stochastic optimization and estimation are investigated, the sufficient attention is paid to nonparametric problems and continuous models with a multidimensional argument. The first chapter is auxiliary. The second one is devoted to investigation of empirical estimates in stochastic optimization problems. In the third chapter parametric regression models are considered, the connection between some of these problems and stochastic optimization problems being studied in the previous chapter is indicated. The fourth chapter is devoted to studying of so-called periodogram estimates | ||
| 650 | 4 | |a Statistics | |
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| 650 | 4 | |a Distribution (Probability theory) | |
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Datensatz im Suchindex
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| author | Knopov, Pavel S. |
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| author_variant | p s k ps psk |
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| dewey-ones | 519 - Probabilities and applied mathematics |
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| dewey-sort | 3519.5 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik |
| doi_str_mv | 10.1007/978-1-4757-3567-3 |
| format | Electronic eBook |
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| id | DE-604.BV042421494 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T01:21:09Z |
| institution | BVB |
| isbn | 9781475735673 9781441952240 |
| issn | 1384-6485 |
| language | English |
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| series | Applied Optimization |
| series2 | Applied Optimization |
| spelling | Knopov, Pavel S. Verfasser aut Empirical Estimates in Stochastic Optimization and Identification by Pavel S. Knopov, Evgeniya J. Kasitskaya Boston, MA Springer US 2002 1 Online-Ressource (VIII, 250 p) txt rdacontent c rdamedia cr rdacarrier Applied Optimization 71 1384-6485 One of the basic problems of statistical investigation is taking the best in some sense decision by observations of some totality of data. In this book empirical methods for solving of stochastic optimization problems and identification methods closely connected with them are investigated. The main attention is paid to studying of asymptotic behavior of the estimates, proving of the assertions about tending of the considered estimates to optimal ones under unlimited increase of the sample size. The sufficiently complete idea on empirical methods in the theory of optimization and estimation can be found in the monographs of Ibragimov and Has'minskii [52], Ermoliev and Wets [105], van de Geer [30], Pfanzagl and Wefelmeyer [107] and many others where the new approach to the investigation for discrete and continuous observations is considered. In the present work some new parametric problems of stochastic optimization and estimation are investigated, the sufficient attention is paid to nonparametric problems and continuous models with a multidimensional argument. The first chapter is auxiliary. The second one is devoted to investigation of empirical estimates in stochastic optimization problems. In the third chapter parametric regression models are considered, the connection between some of these problems and stochastic optimization problems being studied in the previous chapter is indicated. The fourth chapter is devoted to studying of so-called periodogram estimates Statistics Systems theory Mathematical optimization Distribution (Probability theory) Statistics, general Systems Theory, Control Optimization Probability Theory and Stochastic Processes Statistik Stochastische Optimierung (DE-588)4057625-5 gnd rswk-swf Stochastische Optimierung (DE-588)4057625-5 s 1\p DE-604 Kasitskaya, Evgeniya J. Sonstige oth Applied Optimization 71 (DE-604)BV010841718 71 https://doi.org/10.1007/978-1-4757-3567-3 Verlag Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Knopov, Pavel S. Empirical Estimates in Stochastic Optimization and Identification Applied Optimization Statistics Systems theory Mathematical optimization Distribution (Probability theory) Statistics, general Systems Theory, Control Optimization Probability Theory and Stochastic Processes Statistik Stochastische Optimierung (DE-588)4057625-5 gnd |
| subject_GND | (DE-588)4057625-5 |
| title | Empirical Estimates in Stochastic Optimization and Identification |
| title_auth | Empirical Estimates in Stochastic Optimization and Identification |
| title_exact_search | Empirical Estimates in Stochastic Optimization and Identification |
| title_full | Empirical Estimates in Stochastic Optimization and Identification by Pavel S. Knopov, Evgeniya J. Kasitskaya |
| title_fullStr | Empirical Estimates in Stochastic Optimization and Identification by Pavel S. Knopov, Evgeniya J. Kasitskaya |
| title_full_unstemmed | Empirical Estimates in Stochastic Optimization and Identification by Pavel S. Knopov, Evgeniya J. Kasitskaya |
| title_short | Empirical Estimates in Stochastic Optimization and Identification |
| title_sort | empirical estimates in stochastic optimization and identification |
| topic | Statistics Systems theory Mathematical optimization Distribution (Probability theory) Statistics, general Systems Theory, Control Optimization Probability Theory and Stochastic Processes Statistik Stochastische Optimierung (DE-588)4057625-5 gnd |
| topic_facet | Statistics Systems theory Mathematical optimization Distribution (Probability theory) Statistics, general Systems Theory, Control Optimization Probability Theory and Stochastic Processes Statistik Stochastische Optimierung |
| url | https://doi.org/10.1007/978-1-4757-3567-3 |
| volume_link | (DE-604)BV010841718 |
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