000 03710nam a22005055i 4500
001 978-1-84628-329-1
003 DE-He213
005 20161121231114.0
007 cr nn 008mamaa
008 100301s2006 xxk| s |||| 0|eng d
020 _a9781846283291
_9978-1-84628-329-1
024 7 _a10.1007/1-84628-329-9
_2doi
050 4 _aTA1-2040
072 7 _aTBC
_2bicssc
072 7 _aTEC000000
_2bisacsh
082 0 4 _a620
_223
100 1 _aBroersen, Piet M. T.
_eauthor.
245 1 0 _aAutomatic Autocorrelation and Spectral Analysis
_h[electronic resource] /
_cby Piet M. T. Broersen.
264 1 _aLondon :
_bSpringer London,
_c2006.
300 _aXII, 298 p. 104 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
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.
650 0 _aEngineering.
650 0 _aComputers.
650 0 _aImage processing.
650 0 _aStatistics.
650 0 _aComputational intelligence.
650 1 4 _aEngineering.
650 2 4 _aEngineering, general.
650 2 4 _aTheory of Computation.
650 2 4 _aSignal, Image and Speech Processing.
650 2 4 _aStatistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
650 2 4 _aComputational Intelligence.
650 2 4 _aImage Processing and Computer Vision.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9781846283284
856 4 0 _uhttp://dx.doi.org/10.1007/1-84628-329-9
912 _aZDB-2-ENG
950 _aEngineering (Springer-11647)
999 _c508657
_d508657