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PopescuA. N. Kolmogorov and A. N. Shiryayev. Selected Works of A.N. Kolmogorov: Probabilitytheory and mathematical statistics. Springer, 1992. ISBN 9789027727978.T. C. Koopmans. Statistical Inference in Dynamic Economic Models, Cowles CommissionMonograph, No. 10. New York: John Wiley & Sons, 1950.G. Lacerda, P. Spirtes, J. Ramsey, and P. O. Hoyer. Discovering Cyclic Causal Modelsby Independent Component Analysis. In Proceedings of the 24th Conference onUncertainty in Artificial Intelligence (UAI-2008), Helsinki, Finland, 2008.A. D. Lanterman. Schwarz, wallace and rissanen: Intertwining themes in theories ofmodel selection. International Statistical Review, 69(2):185–212, 2001.M. Li and P. M. B. Vitanyi. An introduction to Kolmogorov complexity and its applications,2nd edition. Springer-Verlag, 1997.A. Moneta, D. Entner, P.O. Hoyer, and A. Coad. Causal inference by independent componentanalysis with applications to micro-and macroeconomic data. Jena EconomicResearch Papers, 2010:031, 2010.A. Moneta, N. Chlaß, D. Entner, and P.Hoyer. Causal search in structural vector autoregressionmodels. Journal of Machine Learning Research, Workshop and ConferenceProceedings, XX. Time Series Causality:XX–XX, 2011.G. Nolte, A. Ziehe, V.V. Nikulin, A. Schlögl, N. Krämer, T. Brismar, and K.-R. Müller.Robustly estimating the flow direction of information in complex physical systems.Physical Review Letters, 00(23):234101, 2008.G. Nolte, A. Ziehe, N. Kraemer, F. Popescu, and K.-R. Müller. Comparison of grangercausality and phase slope index. Journal of Machine Learning Research Workshop& Conference Proceedings., Causality: Objectives and Assessment:267:276, 2010.E. Parzen. Long memory of statistical time series modeling. presented at the 2004nber/nsf time series conference at smu, dallas, usa. Technical report, Texas A&MUniversity, http://www.stat.tamu.edu/~eparzen/Long%20Memory%20of%20Statistical%20Time%20Series%20Modeling.pdf, 2004.J. Pearl. Causality: models, reasoning and inference. Cambridge University Press,Cambridge, 2000.K. Pearson. Tables for statisticians and biometricians,. University Press, UniversityCollege, London, [Cambridge Eng., 1930.Peter C.B. Phillips. The problem of identification in finite parameter continous timemodels. Journal of Econometrics, 1:351–362, 1973.F. Popescu. Identification of sparse multivariate autoregressive models. Proceedings ofthe European Signal Processing Conference (EUSIPCO 2008), Lausanne, Switzerland,2008.68
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Causality in Time SeriesVolume 5:Ca
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PrefaceThe following is a print ver
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JMLR: Workshop and Conference Proce
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Linking Granger Causality and the P
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Page 42 and 43: Popescu1. IntroductionCausality is
Page 44 and 45: Popescugeneral terms, if we are pre
Page 46 and 47: PopescuType III error prob. γ = P
Page 48 and 49: Popescuwhich would correspond to a
Page 50 and 51: Popescuvalued vector. A Data Genera
Page 52 and 53: Popescuy i =K∑︁A k y i−k + Bu
Page 54 and 55: Popescuy i =K∑︁A k y i−k + Bu
Page 56 and 57: PopescuFigure 1: SVAR causality and
Page 58 and 59: Popescu6. Spectral methods and phas
Page 60 and 61: PopescuH GCj→i|u = logD i − log
Page 62 and 63: Popescu8.1. The cardinal transform
Page 64 and 65: Popescuy N,i = Bx N,i (37)y = (1
Page 66 and 67: Popescu(a) Unmixed colored noise(b)
Page 68 and 69: PopescuAs we can see in both Figure
Page 70 and 71: Popescuβ > 0 y C,i =x N,i =⎡K∑
Page 72 and 73: Popescupretation of information flo
Page 74 and 75: Popescuit is suggested that a princ
Page 78 and 79: PopescuH. White and X. Lu. Granger
Page 80 and 81: PopescuTable 6: α vs. ΨN * → 50
Page 82 and 83: Roebroeck Seth Valdes-Sosaincreasin
Page 84 and 85: Roebroeck Seth Valdes-SosaFigure 1:
Page 86 and 87: Roebroeck Seth Valdes-Sosasignal fr
Page 88 and 89: Roebroeck Seth Valdes-Sosablood flo
Page 90 and 91: Roebroeck Seth Valdes-Sosaat lower
Page 92 and 93: Roebroeck Seth Valdes-SosaTable 1:
Page 94 and 95: Roebroeck Seth Valdes-Sosa1984) and
Page 96 and 97: Roebroeck Seth Valdes-Sosaanalysis
Page 98 and 99: Roebroeck Seth Valdes-Sosamarginal
Page 100 and 101: Roebroeck Seth Valdes-SosaThe initi
Page 102 and 103: Roebroeck Seth Valdes-Sosaobserved
Page 104 and 105: Roebroeck Seth Valdes-Sosaand corti
Page 106 and 107: Roebroeck Seth Valdes-SosaDaniel Co
Page 108 and 109: Roebroeck Seth Valdes-SosaM. Havlic
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Page 112 and 113: Roebroeck Seth Valdes-SosaV. Walsh
Page 114 and 115: Moneta Chlass Entner HoyerA traditi
Page 116 and 117: Moneta Chlass Entner Hoyerto the
Page 118 and 119: Moneta Chlass Entner Hoyersecond st
Page 120 and 121: Moneta Chlass Entner HoyerThe Wald
Page 122 and 123: Moneta Chlass Entner HoyerStep 3 Ap
Page 124 and 125: Moneta Chlass Entner Hoyerthe struc
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Moneta Chlass Entner Hoyerstricted
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Moneta Chlass Entner Hoyerwhere h 1
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Moneta Chlass Entner Hoyerlatter re
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Moneta Chlass Entner Hoyercausal mo
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Moneta Chlass Entner HoyerL. Baring
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Moneta Chlass Entner HoyerS. Johans
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Moneta Chlass Entner HoyerA. Yatche
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Guyon Statnikov Aliferisis extremel
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Guyon Statnikov Aliferis4. A Data R
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Guyon Statnikov Aliferismethods on
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Guyon Statnikov Aliferisvolume 3, p
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