When the statistics distribution of interested is complex or vague, Bootstrap method may be used to estimate standard error and confidence interval. However, the results derived from parametric and nonparametric Boots...When the statistics distribution of interested is complex or vague, Bootstrap method may be used to estimate standard error and confidence interval. However, the results derived from parametric and nonparametric Bootstrap methods applied to hierarchically structured data may be different. According to the comparing, we found and concluded that nonparametric Bootstrap method is relating seldom affected by the distribution in models. In terms of the nonparametric Bootstrap sampling, the effectiveness of sampling on the highest level unit is more satisfactory than lower level units. The estimation of confidence intervals and hypothesis testing of intraclass corelation coefficients is discussed based on multilevel generalized linear models.展开更多
To systematically incorporate multiple influencing factors,the coupled-state frequency memory(Co-SFM)network is proposed.This model integrates Copula estimation with neural networks,fusing multilevel data information,...To systematically incorporate multiple influencing factors,the coupled-state frequency memory(Co-SFM)network is proposed.This model integrates Copula estimation with neural networks,fusing multilevel data information,which is then fed into downstream learning modules.Co-SFM employs an upstream fusion module to incorporate multilevel data,thereby constructing a macro-plate-micro data structure.This configuration helps identify and integrate characteristics from different data levels,facilitating a deeper understanding of the internal links within the financial system.In the downstream model,Co-SFM uses a state-frequency memory network to mine hidden frequency information within stock prices,and the multifrequency patterns of sequential data are modeled.Empirical results show that Co-SFM s prediction accuracy for stock price trends is significantly better than that of other models.This is especially evident in multistep medium and long-term trend predictions,where integrating multilevel data results in notably improved accuracy.展开更多
文摘When the statistics distribution of interested is complex or vague, Bootstrap method may be used to estimate standard error and confidence interval. However, the results derived from parametric and nonparametric Bootstrap methods applied to hierarchically structured data may be different. According to the comparing, we found and concluded that nonparametric Bootstrap method is relating seldom affected by the distribution in models. In terms of the nonparametric Bootstrap sampling, the effectiveness of sampling on the highest level unit is more satisfactory than lower level units. The estimation of confidence intervals and hypothesis testing of intraclass corelation coefficients is discussed based on multilevel generalized linear models.
基金The National Natural Science Foundation of China(No.72173018).
文摘To systematically incorporate multiple influencing factors,the coupled-state frequency memory(Co-SFM)network is proposed.This model integrates Copula estimation with neural networks,fusing multilevel data information,which is then fed into downstream learning modules.Co-SFM employs an upstream fusion module to incorporate multilevel data,thereby constructing a macro-plate-micro data structure.This configuration helps identify and integrate characteristics from different data levels,facilitating a deeper understanding of the internal links within the financial system.In the downstream model,Co-SFM uses a state-frequency memory network to mine hidden frequency information within stock prices,and the multifrequency patterns of sequential data are modeled.Empirical results show that Co-SFM s prediction accuracy for stock price trends is significantly better than that of other models.This is especially evident in multistep medium and long-term trend predictions,where integrating multilevel data results in notably improved accuracy.
