LEADER 01290nam a2200325 i 4500 001 991002285329707536 005 20020508193629.0 008 990409s1980 uk ||| | eng 020 $a0860780538 035 $ab10986595-39ule_inst 035 $aPARLA158762$9ExL 040 $aDip.to Scienze Storiche Fil. e Geogr.$bita 082 0 $a301.36 100 1 $aHerlihy, David$0158926 245 10$aCities and society in Medieval Italy /$cDavid Herlihy 260 $aLondon :$bVariorum reprints,$c1980 300 $aii, [330] p. ;$c23 cm. 490 0 $aCollected studies series ;$vCS108 650 4$aCittą$zItalia$zToscana$xStoria$yMedioevo 650 4$aCondizioni sociali$zToscana$yMedioevo 650 4$aMedioevo - Citta$xCondizioni sociali$zItalia 650 4$aToscana - Citta$xStoria 650 4$aToscana$xCondizioni sociali 907 $a.b10986595$b23-02-17$c28-06-02 912 $a991002285329707536 945 $aLE009 STOR.33-231$g1$i2009000012059$lle009$o-$pE0.00$q-$rl$s- $t0$u0$v0$w0$x0$y.i11100102$z28-06-02 945 $aLE009 STOR.33-231bis$g2$i2009000012066$lle009$o-$pE0.00$q-$rn$so $t0$u0$v0$w0$x0$y.i11100114$z28-06-02 996 $aCities and society in medieval italy$9475857 997 $aUNISALENTO 998 $ale009$b01-01-99$cm$da $e-$feng$guk $h0$i2 LEADER 05390nam 2200673Ia 450 001 9910784639303321 005 20230120005141.0 010 $a1-281-74938-9 010 $a9786611749385 010 $a0-08-054156-9 035 $a(CKB)1000000000384925 035 $a(EBL)344689 035 $a(OCoLC)476160832 035 $a(SSID)ssj0000249601 035 $a(PQKBManifestationID)12076920 035 $a(PQKBTitleCode)TC0000249601 035 $a(PQKBWorkID)10205972 035 $a(PQKB)10677521 035 $a(MiAaPQ)EBC344689 035 $a(Au-PeEL)EBL344689 035 $a(CaPaEBR)ebr10244424 035 $a(CaONFJC)MIL174938 035 $a(EXLCZ)991000000000384925 100 $a19950622d1995 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 14$aThe spectral analysis of time series$b[electronic resource] /$fLambert H. Koopmans 205 $a[2nd ed.]. 210 $aSan Diego $cAcademic Press$dc1995 215 $a1 online resource (385 p.) 225 1 $aProbability and mathematical statistics ;$vv. 22 300 $aDescription based upon print version of record. 311 $a0-12-419251-3 320 $aIncludes bibliographical references (p. 354-358) and index. 327 $aFront Cover; The Spectral Analysis of Time Series; Copyright Page; Contents; Preface; Acknowledgements; Preface to the Second Edition; Chapter 1. Preliminaries; 1.1 Introduction; 1.2 Time Series and Spectra; 1.3 Summary of Vector Space Geometry; 1.4 Some Probability Notations and Properties; Chapter 2. Models for Spectral Analysis-The Univariate Case; 2.1 Introduction; 2.2 The Wiener Theory of Spectral Analysis; 2.3 Stationary and Weakly Stationary Stochastic Processes; 2.4 The Spectral Representation for Weakly Stationary Stochastic Processes-A Special Case 327 $a2.5 The General Spectral Representation for Weakly Stationary Processes2.6 The Discrete and Continuous Components of the Process; 2.7 Physical Realization of the Different Kinds of Spectra; 2.8 The Real Spectral Representation; 2.9 Ergodicity and the Connection between the Wiener and Stationary Process Theories; 2.10 Statistical Estimation of the Autocovariance and the Mean Ergodic Theorem; Appendix to Chapter 2; Chapter 3. Sampling, Aliasing, and Discrete-Time Models; 3.1 Introduction; 3.2 Sampling and the Aliasing Problem; 3.3 The Spectral Model for Discrete-Time Series 327 $aChapter 4. Linear Filters-General Properties with Applications to Continuous-Time Processes4.1 Introduction; 4.2 Linear Filters; 4.3 Combining Linear Filters; 4.4 Inverting Linear Filters; 4.5 Nonstationary Processes Generated by Time Varying Linear Filters; Appendix to Chapter 4; Chapter 5. Multivariate Spectral Models and Their Applications; 5.1 Introduction; 5.2 The Spectrum of a Multivariate Time Series-Wiener Theory; 5.3 Multivariate Weakly Stationary Stochastic Processes; 5.4 Linear Filters for Multivariate Time Series 327 $a5.5 The Bivariate Spectral Parameters, Their Intepretations and Uses5.6 The Multivariate Spectral Parameters, Their Interpretations and Uses; Appendix to Chapter 5; Chapter 6. Digital Filters; 6.1 Introduction; 6.2 General Properties of Digital Filters; 6.3 The Effect of Finite Data Length; 6.4 Digital Filters with Finitely Many Nonzero Weights; 6.5 Digital Filters Obtained by Combining Simple Filters; 6.6 Filters with Gapped Weights and Results Concerning the Filtering of Series with Polynomial Trends; Appendix to Chapter 6 327 $aChapter 7. Finite Parameter Models, Linear Prediction, and Real-Time Filtering7.1 Introduction; 7.2 Moving Averages; 7.3 Autoregressive Processes; 7.4 The Linear Prediction Problem; 7.5 Mixed Autoregressive-Moving Average Processes and Recursive Prediction; 7.6 Linear Filtering in Real Time; Appendix to Chapter 7; Chapter 8. The Distribution Theory of Spectral Estimates with Applications to Statistical Inference; 8.1 Introduction; 8.2 Distribution of the Finite Fourier Transform and the Periodogram; 8.3 Distribution Theory for Univariate Spectral Estimators 327 $a8.4 Distribution Theory for Multivariate Spectral Estimators with Applications to Statistical Inference 330 $aTo tailor time series models to a particular physical problem and to follow the working of various techniques for processing and analyzing data, one must understand the basic theory of spectral (frequency domain) analysis of time series. This classic book provides an introduction to the techniques and theories of spectral analysis of time series. In a discursive style, and with minimal dependence on mathematics, the book presents the geometric structure of spectral analysis. This approach makes possible useful, intuitive interpretations of important time series parameters and provides a unifi 410 0$aProbability and mathematical statistics ;$vv. 22. 606 $aSpectral theory (Mathematics) 606 $aTime-series analysis 615 0$aSpectral theory (Mathematics) 615 0$aTime-series analysis. 676 $a519.5/5 676 $a519.55 700 $aKoopmans$b Lambert Herman$f1930-$0252848 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910784639303321 996 $aSpectral analysis of time series$9439719 997 $aUNINA LEADER 01613oas 2200625 a 450 001 9910144638803321 005 20251106213014.0 035 $a(OCoLC)62890579 035 $a(CONSER) 2006212731 035 $a(CKB)1000000000223811 035 $a(DE-599)ZDB2238592-7 035 $a(MiAaPQ)2037516 035 $a(EXLCZ)991000000000223811 100 $a20060113a20069999 sy a 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aEmpirical musicology review $eEMR 210 $aColumbus, OH $cEmpirical Musicology Review$d[2006]- 215 $a1 online resource 300 $aRefereed/Peer-reviewed 300 $aTitle from title screen (publisher's Web site viewed Dec. 21, 2006). 311 08$a1559-5749 517 3 $aEMR 606 $aMusicology$vPeriodicals 606 $aMusicologie$vPe?riodiques 606 $aMusicology$2fast$3(OCoLC)fst01030893 608 $aPeriodicals.$2fast 615 0$aMusicology 615 6$aMusicologie 615 7$aMusicology. 676 $a780 712 02$aOhio State University.$bSchool of Music. 801 0$bNSD 801 1$bNSD 801 2$bDLC 801 2$bMUQ 801 2$bCUS 801 2$bOCLCQ 801 2$bOCLCF 801 2$bCUS 801 2$bOCLCQ 801 2$bVT2 801 2$bOCLCQ 801 2$bOCLCO 801 2$bOCLCQ 801 2$bAUD 801 2$bOCLCL 801 2$bOCLCQ 906 $aJOURNAL 912 $a9910144638803321 996 $aEmpirical musicology review$92115649 997 $aUNINA