| Detecting Lags in Nonlinear Models Using General Mutual Information |
| Received:December 11, 2007 Revised:January 05, 2009 |
| Key Words:
general mutual information general conditional mutual information nonlinear time series lag dependence.
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| Fund Project: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 Name | Affiliation | | 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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