• DocumentCode
    2481766
  • Title

    A Test of Granger Non-causality Based on Nonparametric Conditional Independence

  • Author

    Seth, Sohan ; Principe, Jose C.

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Univ. of Florida, Gainesville, FL, USA
  • fYear
    2010
  • fDate
    23-26 Aug. 2010
  • Firstpage
    2620
  • Lastpage
    2623
  • Abstract
    In this paper we describe a test of Granger non-causality from the perspective of a new measure of nonparametric conditional independence. We apply the proposed test on two synthetic nonlinear problems where linear Granger causality fails and show that the proposed method is able to derive the true causal connectivity effectively.
  • Keywords
    stochastic processes; granger noncausality; nonparametric conditional independence; synthetic nonlinear problems; Accuracy; Biological system modeling; Couplings; Distribution functions; Kernel; Time series analysis; Yttrium; Granger causality; conditional independence; kernel methods; regression;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Pattern Recognition (ICPR), 2010 20th International Conference on
  • Conference_Location
    Istanbul
  • ISSN
    1051-4651
  • Print_ISBN
    978-1-4244-7542-1
  • Type

    conf

  • DOI
    10.1109/ICPR.2010.642
  • Filename
    5595993
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2481766