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008			170310s2017 sz a ob 000 0 eng d
020			_a3319492861 _q(electronic bk.)
020			_a9783319492865 _q(electronic bk.)
020			_z3319492853
020			_z9783319492858 _q(print)
040			_aN$T _cN$T _dIDEBK _dGW5XE _dN$T _dYDX _dEBLCP _dOCLCF _dUAB _dNJR _dIOG _dCOO _dMERER _dESU _dVT2 _dOCLCQ _dJBG _dIAD _dICW _dICN _dOTZ _dOCLCQ _dAZU _dUPM _dOCLCQ _dU3W _dES-MaUEC _bspa
050		4	_aQA277 _b.G854 2017 EB
066			_c(S
100	1		_aGül, Gökhan, _eautor
245	1	0	_aRobust and distributed hypothesis testing _cGökhan Gül.
264		1	_aCham, Switzerland _bSpringer _c2017.
300			_a1 recurso en línea (xxi, 141 páginas) _bilustraciones (algunas a color)
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	0		_aLecture notes in electrical engineering _vvolume 414
500			_aSpringerLink _bSpringer Engineering eBooks 2017 English+International
504			_aIncluye referencias bibliográficas
505	0		_6880-01 _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.
520	3		_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		7	_aEstadística matemática _2embne _0(OCoLC)fst01132063 _0 _9138936
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=http://link.springer.com/10.1007/978-3-319-49286-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
880	8		_6505-00/(S _a3.2.3 Limiting Robustness Parameters3.2.4 Limiting Test; 3.3 Minimax Robust Hypothesis Testing with KL-Divergence; 3.3.1 Saddle Value Specification; 3.3.2 Problem Definition; 3.3.3 Derivation of LFDs and the Robust Decision Rule; 3.3.4 Distribution of the Log-Likelihood Ratios of LFDs; 3.3.5 Monotonicity of KL-Divergence; 3.3.6 Symmetric Density Functions; 3.3.7 Limiting Robustness Parameters; 3.3.8 Limiting Test; 3.4 Other Distances; 3.4.1 The χ2- and squared Hellinger distance; 3.4.2 Symmetrized χ2- distance; 3.4.3 Symmetrized KL-divergence; 3.5 Asymptotically Robust Hypothesis Test.
880	8		_6505-00/(S _a3.5.1 Limiting Test3.6 Simulations; 3.6.1 Theoretical Examples; 3.7 Conclusions; References; 4 Robust Hypothesis Testing with Multiple Distances; 4.1 Introduction; 4.2 Huber's Generalized Minimax Robust Hypothesis Test; 4.2.1 Distributions of the Log-Likelihood Ratios of LFDs; 4.3 Robust Hypothesis Testing with α-Divergence; 4.3.1 Saddle Value Specification; 4.3.2 Problem Definition; 4.3.3 Derivation of LFDs and the Robust Decision Rule; 4.3.4 Distributions of the Log-Likelihood Ratios of LFDs; 4.3.5 Simplified Model with Additional Constraints; 4.3.6 Limiting Robustness Parameters.
880	8		_6505-00/(S _a4.3.7 Limiting Test4.4 Robust Hypothesis Testing with Composite Distances; 4.4.1 Composite Uncertainty Model; 4.4.2 Existence of Least Favorable Distributions; 4.4.3 Two Examples of the Composite Test; 4.5 Simulations; 4.5.1 Theoretical Examples; 4.5.2 Signal Processing Example: Spectrum Sensing; 4.6 Conclusions; References; 5 Robust Hypothesis Testing with Repeated Observations; 5.1 Introduction; 5.2 Robust Fixed Sample Size Tests; 5.2.1 Fixed Sample Size (h)-Test; 5.2.2 Fixed Sample Size (m)α-Test; 5.2.3 Fixed Sample Size (c)-Test; 5.2.4 Asymptotic Performance Analysis.
880	8		_6505-00/(S _a5.3 Robust Sequential Probability Ratio Tests5.3.1 Sequential (h)-Test; 5.3.2 Sequential (m)α- and (c)-Test; 5.3.3 Sequential (a)-Test; 5.4 An Extension of the Composite Model to Robust Estimation Problems; 5.5 Simulations; 5.5.1 Theoretical Examples; 5.5.2 Signal Processing Example: Target Image Classification; 5.6 Conclusions; References; 6 Robust Decentralized Hypothesis Testing; 6.1 Introduction; 6.2 System Specification and Problem Definition; 6.3 General Solutions to Robust Decentralized Detection Problem; 6.4 Specific Examples; 6.4.1 Huber's Extended Uncertainty Class.
880	8		_6505-01/(S _a6.4.2 Uncertainty Classes Based on α-Divergence.
988			_aEBOOK, asignarmaterias, EBSPRINGER_2017C
998			_b02/2018 _dz _e- _zSI
999			_c95539 _d95539 _x1