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000780071 1001_ $$aGül, Gökhan,$$eauthor.
000780071 24510 $$aRobust and distributed hypothesis testing /$$cGökhan Gül.
000780071 264_1 $$aCham, Switzerland :$$bSpringer,$$c2017.
000780071 300__ $$a1 online resource (xxi, 141 pages) :$$billustrations.
000780071 336__ $$atext$$btxt$$2rdacontent
000780071 337__ $$acomputer$$bc$$2rdamedia
000780071 338__ $$aonline resource$$bcr$$2rdacarrier
000780071 4901_ $$aLecture notes in electrical engineering ;$$vvolume 414
000780071 504__ $$aIncludes bibliographical references.
000780071 5050_ $$aForeword; Acknowledgments; Contents; About the Author; Acronyms; Symbols; 1 Introduction; 1.1 Motivation; 1.2 Related Work; 1.3 Contributions; 1.3.1 Publications; 1.4 Book Overview; References; 2 Background; 2.1 Introduction; 2.2 Robust Detection; 2.2.1 Minimax Hypothesis Testing; 2.2.2 Robust Hypothesis Testing; 2.3 Decentralized Detection; 2.4 Conclusions; References; 3 Robust Hypothesis Testing with a Single Distance; 3.1 Introduction; 3.2 Huber's Minimax Robust Hypothesis Test; 3.2.1 LFDs and the Existence of Saddle Value; 3.2.2 Distributions of the Log-Likelihood Ratios of LFDs
000780071 506__ $$aAccess limited to authorized users.
000780071 520__ $$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.
000780071 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed March 21, 2017).
000780071 650_0 $$aStatistical hypothesis testing.
000780071 77608 $$iPrint version:$$z3319492853$$z9783319492858$$w(OCoLC)960837878
000780071 830_0 $$aLecture notes in electrical engineering ;$$vv. 414.
000780071 852__ $$bebk
000780071 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-319-49286-5$$zOnline Access$$91397441.1
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