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020 _a9783319492865
_9978-3-319-49286-5
024 7 _a10.1007/978-3-319-49286-5
_2doi
050 4 _aTK5102.9
072 7 _aTJF
_2bicssc
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082 0 4 _a621.382
_223
100 1 _aGül, Gökhan.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_957556
245 1 0 _aRobust and Distributed Hypothesis Testing
_h[electronic resource] /
_cby Gökhan Gül.
250 _a1st ed. 2017.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2017.
300 _aXXI, 141 p. 47 illus., 42 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes in Electrical Engineering,
_x1876-1119 ;
_v414
505 0 _aIntroduction -- Background -- Robust Hypothesis Testing with a Single Distance -- Robust Hypothesis Testing with Multiple Distances -- Robust Hypothesis Testing with Repeated Observations -- Robust Decentralized Hypothesis Testing -- Minimax Decentralized Hypothesis Testing -- Conclusions and Outlook.
520 _aThis 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 robust hypothesis testing, this book is mainly intended for postgraduates and researchers in the field of electrical and electronic engineering, statistics and applied probability. Moreover, it may be of interest for students and researchers working in the field of classification, pattern recognition and cognitive radio.
650 0 _aSignal processing.
_94052
650 0 _aStatistics .
_931616
650 0 _aPattern recognition systems.
_93953
650 1 4 _aSignal, Speech and Image Processing .
_931566
650 2 4 _aStatistical Theory and Methods.
_931618
650 2 4 _aAutomated Pattern Recognition.
_931568
710 2 _aSpringerLink (Online service)
_957557
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319492858
776 0 8 _iPrinted edition:
_z9783319492872
776 0 8 _iPrinted edition:
_z9783319841229
830 0 _aLecture Notes in Electrical Engineering,
_x1876-1119 ;
_v414
_957558
856 4 0 _uhttps://doi.org/10.1007/978-3-319-49286-5
912 _aZDB-2-ENG
912 _aZDB-2-SXE
942 _cEBK
999 _c79968
_d79968