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008			170228s2017 sz a o 100 0 eng d
020			_a3319514458 _q(electronic bk.)
020			_a9783319514451 _q(electronic bk.)
020			_z331951444X
020			_z9783319514444 _q(print)
040			_aN$T _cN$T _dIDEBK _dGW5XE _dN$T _dYDX _dEBLCP _dOCLCQ _dNJR _dOCLCF _dCOO _dIOG _dAZU _dUWO _dVT2 _dUPM _dESU _dZ5A _dJBG _dIAD _dICW _dICN _dOTZ _dOCLCQ _dU3W _dES-MaUEC _bspa
050		4	_aTK5102.9 _bC935 2017 EB
111	2		_aWorkshop on Cyclostationary Systems and Their Applications _n(9th : _d2016 : _cGrudki, Poland)
245	1	0	_aCyclostationarity : _btheory and methods III : contributions to the 9th Workshop on Cyclostationary Systems and Their Applications, Grodek, Poland, 2016 _cFakher Chaari, Jacek Leskow, Antonio Napolitano, Radoslaw Zimroz, Agnieszka Wylomanska, editors.
264		1	_aCham, Switzerland _bSpringer _c[2017]
300			_a1 recurso en línea (viii, 257 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		_aApplied condition monitoring _x2363-698X _vvolume 6
500			_aSpringerLink _bSpringer Engineering eBooks 2017 English+International
505	0		_aIntroduction; Weak Dependence: An Introduction Through Asymmetric ARCH Models; 1 Introduction; 2 Weak Dependence; 3 Models with Infinite Memory; 3.1 Assumptions; 3.2 Properties of the Stationary Solution; 3.3 Asymptotic Results; 4 Asymmetric ARCH(1)-model; 4.1 Existence of a Stationary Solution; 5 Moment Estimators; 5.1 Explicit Moments; 5.2 Moment Based Estimation; 5.3 Asymptotic Considerations; 6 Estimating the Density of the Residuals; 6.1 Fitted Residuals; 6.2 Density Estimation; References.
505	8		_a3.3 Model Fitting4 Simulation Study; 5 Real Data Analysis; 6 Conclusion; References; GARCH Process with GED Distribution; 1 Introduction; 2 Theory; 3 Testing and Estimation; 3.1 Correlation Testing; 4 Prediction; 5 Applications; 6 Conclusions; References; A Residual Based Method for Fitting PAR Models Using Fourier Representation of Periodic Coefficients; 1 Introduction; 2 Determination of PAR Coefficients by Yule Walker Method; 3 OLS Fit for Periodic Function with Additive Noise; 4 OLS fit of a Fourier Series Parametrization of a PAR Model; 5 Conclusions; References.
505	8		_a4 Final ConclusionsReferences; 4 Seismic Signal Enhancement via AR Filtering and Spatial Time-Frequency Denoising; Abstract; 1 Introduction; 2 Methodology; 2.1 Autoregressive Filter; 2.2 Algorithm of Enhancement via AR Filtering; 2.3 Algorithm of Spatial Time-Frequency Denoising; 2.4 AR Order and Reference Noise Length Selection Based on AIC; 3 Simulation Results; 4 Real Data Results; 5 Conclusions; References; Transformed GARMA Model with the Inverse Gaussian Distribution; 1 Introduction; 2 Transformations; 3 TGARMA Model Fitting; 3.1 Model Definition; 3.2 Inverse Gaussian TGARMA Model.
505	8		_a8 Vectorial Periodically Correlated Random Processes and Their Covariance Invariant AnalysisAbstract; 1 Introduction; 2 The First and the Second Order Moment Functions; 2.1 The Vector of a Mean Function; 2.2 The Covariance Tensor-Function; 3 The Linear Invariants of the Covariance Tensor-Function; 4 The Quadratic Invariant Properties; 5 The Harmonic Analysis of the Covariance Tensor-Functions and Their Invariants; 6 Invariant Covariance Analysis of Modulated Signals; 7 The Examples of Using the Covariance Invariants for Vibration Analysis; 8 Conclusions; References.
505	8		_aSubsampling for Non-stationary Time Series with Long Memory and Heavy Tails Using Weak Dependence Condition1 Introduction; 2 Basic Concepts and Definitions; 3 The Model and Its Properties; 4 Central Limit Theorems in the GED Case; 5 Consistency of the Subsampling Method for the Mean; 6 Conclusions; References; Change-Point Problem in the Fraction-Of-Time Approach; 1 Introduction; 2 Change-Point Problem in the FOT Approach; 2.1 The Nonstochastic Idea of FOT; 2.2 Change-Point Detection Methodology; 3 Simulation Study; 3.1 Data Sets Description; 3.2 Change-Point Detection -- Simulations Results.
520	3		_aThis book gathers contributions presented at the 9th Workshop on Cyclostationary Systems and Their Applications, held in Gródek nad Dunajcem, Poland in February 2016. It includes both theory-oriented and practice-oriented chapters. The former focus on heavy-tailed time series and processes, PAR models, rational spectra for PARMA processes, covariance invariant analysis, change point problems, and subsampling for time series, as well as the fraction-of-time approach, GARMA models and weak dependence. In turn, the latter report on case studies of various mechanical systems, and on stochastic and statistical methods, especially in the context of damage detection. The book provides students, researchers and professionals with a timely guide to cyclostationary systems, nonstationary processes and relevant engineering applications.
650		7	_aOndas _2embne _0(OCoLC)fst00886010 _0 _9138348
700	1		_aChaari, Fakher _eeditor literario _997066
700	1		_aLeskow, Jacek, _eeditor literario
700	1		_aNapolitano, Antonio, _d1964- _eeditor literario
700	1		_aWylomanska, Agnieszka, _eeditor literario
700	1		_aZimroz, Radoslaw _eeditor literario _997067
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=http://link.springer.com/10.1007/978-3-319-51445-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
988			_aEBOOK, asignarmaterias, EBSPRINGER_2017C
998			_b02/2018 _dz _e- _zSI
999			_c95473 _d95473 _x1