MARC Record from marc_columbia

LEADER: 04375cam a2200625Ia 4500
001 15091936
005 20220703232657.0
006 m o d
007 cr bn|||||||||
008 130727s2014 flua ob 001 0 eng d
035 $a(OCoLC)ocn854520284
035 $a(NNC)15091936
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019 $a851155299$a859596117$a860709919$a988520570$a991997200$a994914441$a1031054486$a1062903835$a1086453214
020 $a9781420010060$q(electronic bk.)
020 $a1420010069$q(electronic bk.)
020 $a1299704018
020 $a9781299704015
020 $z9781584881766$q(hardback)
020 $z1584881763$q(hardback)
024 7 $a10.1201/b15154$2doi
035 $a(OCoLC)854520284$z(OCoLC)851155299$z(OCoLC)859596117$z(OCoLC)860709919$z(OCoLC)988520570$z(OCoLC)991997200$z(OCoLC)994914441$z(OCoLC)1031054486$z(OCoLC)1062903835$z(OCoLC)1086453214
037 $aCL0500000310$bSafari Books Online
050 4 $aQA280$b.L49 2014eb
072 7 $aMAT$x003000$2bisacsh
072 7 $aMAT$x029000$2bisacsh
082 04 $a519.5/5$a519.54
084 $aMAT029000$aMAT029010$aTEC007000$2bisacsh
049 $aZCUA
100 1 $aLi, Ta-Hsin,$eauthor.
245 10 $aTime series with mixed spectra /$cTa-Hsin Li.
260 $aBoca Raton, FL :$bCRC Press,$c©2014.
300 $a1 online resource (x, 670 pages) :$billustrations
336 $atext$btxt$2rdacontent
337 $acomputer$bc$2rdamedia
338 $aonline resource$bcr$2rdacarrier
347 $adata file$2rda
504 $aIncludes bibliographical references and index.
505 0 $aIntroduction -- Basic concepts -- Cramér-Rao lower bound -- Autocovariance function -- Linear regression analysis -- Fourier analysis approach -- Estimation of noise spectrum -- Maximum likelihood approach -- Autoregressive approach -- Covariance analysis approach -- Further Topics.
520 $a"Time series with mixed spectra are characterized by hidden periodic components buried in random noise. Despite strong interest in the statistical and signal processing communities, no book offers a comprehensive and up-to-date treatment of the subject. Filling this void, Time Series with Mixed Spectra focuses on the methods and theory for the statistical analysis of time series with mixed spectra. It presents detailed theoretical and empirical analyses of important methods and algorithms. Using both simulated and real-world data to illustrate the analyses, the book discusses periodogram analysis, autoregression, maximum likelihood, and covariance analysis. It considers real- and complex-valued time series, with and without the Gaussian assumption. The author also includes the most recent results on the Laplace and quantile periodograms as extensions of the traditional periodogram. Complete in breadth and depth, this book explains how to perform the spectral analysis of time series data to detect and estimate the hidden periodicities represented by the sinusoidal functions. The book not only extends results from the existing literature but also contains original material, including the asymptotic theory for closely spaced frequencies and the proof of asymptotic normality of the nonlinear least-absolute-deviations frequency estimator."--Publisher's website.
588 0 $aPrint version record.
650 0 $aSpectrum analysis.
650 0 $aTime-series analysis.
650 6 $aSérie chronologique.
650 7 $aMATHEMATICS$xApplied.$2bisacsh
650 7 $aMATHEMATICS$xProbability & Statistics$xGeneral.$2bisacsh
650 7 $aSpectrum analysis.$2fast$0(OCoLC)fst01129108
650 7 $aTime-series analysis.$2fast$0(OCoLC)fst01151190
655 0 $aElectronic books.
655 4 $aElectronic books.
776 08 $iPrint version:$aLi, Ta-Hsin.$tTime series with mixed spectra.$dBoca Raton, FL : CRC Press, [2014]$z9781584881766$w(DLC) 2013014918$w(OCoLC)845349490
856 40 $uhttp://www.columbia.edu/cgi-bin/cul/resolve?clio15091936$zTaylor & Francis eBooks
852 8 $blweb$hEBOOKS