Detecting Lags in Nonlinear Models Using General Mutual Information
Received:December 11, 2007  Revised:January 05, 2009
Key Word: general mutual information   general conditional mutual information   nonlinear time series   lag dependence.  
Fund ProjectL:Supported by the National Natural Science Foundation of China (Grant Nos.60375003; 60972150) and the Science and Technology Innovation Foundation of Northwestern Polytechnical University (Grant No.2007KJ01033).
Author NameAffiliation
Wei GAO Department of Applied Mathematics, Northwest Polytechnical University, Shaanxi 710072, P. R. China
School of Statistics, Xi'an University of Finance & Economics, Shaanxi 710061, P. R. China 
Zheng TIAN Department of Applied Mathematics, Northwest Polytechnical University, Shaanxi 710072, P. R. China
National Key Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100080, P. R. China 
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Abstract:
      The general mutual information (GMI) and general conditional mutual information (GCMI) are considered to measure lag dependences in nonlinear time series. Both of the measures have the property of invariance with transform. The statistics based on GMI and GCMI are estimated using the correlation integral. Under the hypothesis of independent series, the estimators have Gaussian asymptotic distributions. Simulations applied to generated nonlinear series demonstrate that the methods appear to find frequently the correct lags.
Citation:
DOI:10.3770/j.issn:1000-341X.2010.01.008
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