| 非线性模型滞后相依的广义互信息检验 |
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| 引用本文： | 高伟,田铮.非线性模型滞后相依的广义互信息检验[J].数学研究与评论,2010,30(1):87-98. |
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| 作者姓名： | 高伟 田铮 |
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| 作者单位： | 西北工业大学应用数学系, 陕西 西安 710072; 西安财经学院统计学院, 陕西 西安 710061;西北工业大学应用数学系, 陕西 西安 710072; 中国科学院自动化研究所模式识别国家重点实验室, 北京 100080 |
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| 基金项目： | 国家自然科学基金(Nos.60375003; 60972150);西北工业大学创新基金(No.2007KJ01033). |
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| 摘 要： | 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.
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| 关 键 词： | 非线性模型 互信息 滞后 非线性时间序列 检测 高斯分布 模拟应用 GMI |
| 收稿时间： | 2007/12/11 0:00:00 |
| 修稿时间： | 1/5/2009 12:00:00 AM |
Detecting Lags in Nonlinear Models Using General Mutual Information |
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| Wei GAO and Zheng TIAN.Detecting Lags in Nonlinear Models Using General Mutual Information[J].Journal of Mathematical Research and Exposition,2010,30(1):87-98. |
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| Authors: | Wei GAO and Zheng TIAN |
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| Institution: | 1. 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
2. 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 |
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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. |
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| Keywords: | general mutual information general conditional mutual information nonlinear time series lag dependence |
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