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001			291409
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007			cr nn 008mamaa
008			150903s2006 xxk| o |||| 0|eng d
020			_a9781846283291 _99781846283291
024	7		_a10.1007/1846283299 _2doi
035			_avtls000343807
039		9	_a201509030751 _bVLOAD _c201404121006 _dVLOAD _c201404090744 _dVLOAD _y201402061205 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
050		4	_aTK5102.9
100	1		_aBroersen, Piet M. T. _eautor _9322687
245	1	0	_aAutomatic Autocorrelation and Spectral Analysis / _cby Piet M. T. Broersen.
264		1	_aLondon : _bSpringer London, _c2006.
300			_axii, 298 páginas 104 ilustraciones _brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
500			_aSpringer eBooks
505	0		_aBasic Concepts -- Periodogram and Lagged Product Autocorrelation -- ARMA Theory -- Relations for Time Series Models -- Estimation of Time Series Models -- AR Order Selection -- MA and ARMA Order Selection -- ARMASA Toolbox with Applications -- Advanced Topics in Time Series Estimation.
520			_aAutomatic Autocorrelation and Spectral Analysis gives random data a language to communicate the information they contain objectively. In the current practice of spectral analysis, subjective decisions have to be made all of which influence the final spectral estimate and mean that different analysts obtain different results from the same stationary stochastic observations. Statistical signal processing can overcome this difficulty, producing a unique solution for any set of observations but that solution is only acceptable if it is close to the best attainable accuracy for most types of stationary data. Automatic Autocorrelation and Spectral Analysis describes a method which fulfils the above near-optimal-solution criterion. It takes advantage of greater computing power and robust algorithms to produce enough candidate models to be sure of providing a suitable candidate for given data. Improved order selection quality guarantees that one of the best (and often the best) will be selected automatically. The data themselves suggest their best representation. Should the analyst wish to intervene, alternatives can be provided. Written for graduate signal processing students and for researchers and engineers using time series analysis for practical applications ranging from breakdown prevention in heavy machinery to measuring lung noise for medical diagnosis, this text offers: • tuition in how power spectral density and the autocorrelation function of stochastic data can be estimated and interpreted in time series models; • extensive support for the MATLAB® ARMAsel toolbox; • applications showing the methods in action; • appropriate mathematics for students to apply the methods with references for those who wish to develop them further.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9781846283284
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/1-84628-329-9 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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