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非线性模型滞后相依的广义互信息检验 总被引:1,自引:0,他引:1
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. 相似文献
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We present a novel perspective on characterizing the spectral correspondence between nodes of the weighted graph with application to image registration. It is based on matrix perturbation analysis on the spectral graph. The contribution may be divided into three parts. Firstly, the perturbation matrix is obtained by perturbing the matrix of graph model. Secondly, an orthogonal matrix is obtained based on an optimal parameter, which can better capture correspondence features. Thirdly, the optimal matching matrix is proposed by adjusting signs of orthogonal matrix for image registration. Experiments on both synthetic images and real-world images demonstrate the effectiveness and accuracy of the proposed method. 相似文献
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两类索赔相关风险模型的罚金折现期望函数 总被引:2,自引:0,他引:2
考虑两类索赔相关风险模型.两类索赔计数过程分别为独立的广义Poisson过程和广义Erlang(2)过程.得到了该风险模型的罚金折现期望函数满足的积分微分方程及该函数的Laplace变换的表达式,且当索赔额均服从指数分布时,给出了罚金折现期望函数及破产概率的明确表达式. 相似文献
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根据我国1998年1月至2005年7月的进出口贸易数据,使用协整分析法检验了进口和出口之间的均衡关系,建立误差修正模型,发现进口和出口的互为Granger原因.实证分析结果表明我国在加入WTO之后,进出口之间的弹性得到了提高,对外贸易机制发生了变化。 相似文献
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周期异方差时间序列的季节单位根检验 总被引:1,自引:1,他引:0
为检验带异方差的季节时间序列中的单位根,提出基于最小二乘估计的统计量.在原假设下得到检验统计量的极限分布.用M on te C arlo方法计算同方差下的经验分位数并考虑异方差对检验水平的影响.实例分析表明了用该方法检验季节单位根的有效性. 相似文献
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多供应商条件下选址模型的一个研究结果 总被引:5,自引:0,他引:5
通过实例和对模型的分析,得出[1]中已有多供应商条件上所建模型的不合理性,并提出了模型合理性的原则。 相似文献
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传统的两变量Granger因果分析法容易产生伪因果关系,且不能刻画变量间的同期因果性.利用图模型方法研究多维时间序列变量间Granger因果关系,通过Granger因果图的建立将问题转化为Granger因果图结构的辨识问题,利用局部密度估计法构造相应的辨识统计量,采用bootstrap方法来确定检验统计量的原分布.模拟分析以及对于中国股市间Granger因果关系的研究说明了该方法的有效性. 相似文献
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Blind noisy image separation based on a new robust independent component analysis network 总被引:1,自引:0,他引:1
The separation of noisy image is a very exciting area of research, especially when no prior information is available about the noisy image. In this paper, we propose a robust independent component analysis (ICA) network for separation images contaminated with high-level additive noise or outliers. We reduce the power of additive noise by adding outlier rejection rule in ICA. Extensive computer simulations confirm robustness and the excellent performance of the resulting algorithms. 相似文献