| 書目名稱 | Robust and Distributed Hypothesis Testing |
| 編輯 | G?khan Gül |
| 視頻video | http://file.papertrans.cn/832/831377/831377.mp4 |
| 概述 | Reports on novel schemes for minimax robust hypothesis testing and decentralized detection.Provides tools for dealing with modeling errors and outliers.Discusses applications to spectrum sensing, clas |
| 叢書名稱 | Lecture Notes in Electrical Engineering |
| 圖書封面 |  |
| 描述 | .This book generalizes and extends the available theory in robust and decentralized hypothesis testing. In particular, it presents a robust test for modeling errors which is independent from the assumptions that a sufficiently large number of samples is available, and that the distance is the KL-divergence. Here, the distance can be chosen from a much general model, which includes the KL-divergence as a very special case. This is then extended by various means. A minimax robust test that is robust against both outliers as well as modeling errors is presented. Minimax robustness properties of the given tests are also explicitly proven for fixed sample size and sequential probability ratio tests. The theory of robust detection is extended to robust estimation and the theory of robust distributed detection is extended to classes of distributions, which are not necessarily stochastically bounded. It is shown that the quantization functions for the decision rules can also be chosen as non-monotone. Finally, the book describes the derivation of theoretical bounds in minimax decentralized hypothesis testing, which have not yet been known. As a timely report on the state-of-the-art in robu |
| 出版日期 | Book 2017 |
| 關(guān)鍵詞 | Distributed Detection; Signal Detection; Parameter Estimation; Data Fusion; Sensor Networks; Minimax Hypo |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-319-49286-5 |
| isbn_softcover | 978-3-319-84122-9 |
| isbn_ebook | 978-3-319-49286-5Series ISSN 1876-1100 Series E-ISSN 1876-1119 |
| issn_series | 1876-1100 |
| copyright | Springer International Publishing AG 2017 |
|Archiver|手機(jī)版|小黑屋|
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