In this paper, we investigate the empirical likelihood diagnosis of modal linear regression models. The empirical likelihood ratio function based on modal regression estimation method for the regression coefficient is...In this paper, we investigate the empirical likelihood diagnosis of modal linear regression models. The empirical likelihood ratio function based on modal regression estimation method for the regression coefficient is introduced. First, the estimation equation based on empirical likelihood method is established. Then, some diagnostic statistics are proposed. At last, we also examine the performance of proposed method for finite sample sizes through simulation study.展开更多
针对滑坡位移难以预测、影响因素难以选择等问题,提出一种结合了二次移动平均(DMA)法、变分模态分解(VMD)、改进灰狼优化(IGWO)算法与支持向量回归(SVR)的模型进行滑坡位移预测。首先,利用DMA提取滑坡位移趋势项和周期项,采用多项式拟...针对滑坡位移难以预测、影响因素难以选择等问题,提出一种结合了二次移动平均(DMA)法、变分模态分解(VMD)、改进灰狼优化(IGWO)算法与支持向量回归(SVR)的模型进行滑坡位移预测。首先,利用DMA提取滑坡位移趋势项和周期项,采用多项式拟合对趋势项进行预测;其次,对滑坡周期项的影响因素进行分类,采用VMD对原始影响因子序列进行分解获得最优序列;再次,提出一种结合SVR与基于改进Circle多策略的灰狼优化算法CTGWO-SVR(Circle Tactics Grey Wolf Optimizer with SVR)对滑坡周期项进行预测;最后采用时间序列加法模型求出累计位移预测序列,并采用灰色预测的后验证差校验和小概率误差对模型进行评价。实验结果表明,与GA-SVR和GWO-SVR模型相比,CTGWO-SVR的预测精度更高,拟合度达到0.979,均方根误差分别减小了51.47%与59.25%,预测精度等级为一级,可满足滑坡预测的实时性和准确性要求。展开更多
目的:观察不同输血方式对高龄骨科手术患者术后转归的影响。方法:将90例择期骨科手术患者根据术中不同输血模式分为异体输血组(A组)和自体输血组(B组),自体+异体输血组(C组),每组各30例。患者全身麻醉后,桡动脉采血检测Hb、Hct值;观察...目的:观察不同输血方式对高龄骨科手术患者术后转归的影响。方法:将90例择期骨科手术患者根据术中不同输血模式分为异体输血组(A组)和自体输血组(B组),自体+异体输血组(C组),每组各30例。患者全身麻醉后,桡动脉采血检测Hb、Hct值;观察并记录术中失血量、异体输血量、自体输血量、尿量、手术时间及液体输入量;分别于术前、术后、术后1 d、3 d、5 d采集静脉血用流式细胞仪检测CD3^(+)、CD4^(+)及NK细胞百分比;术前1 d、术后1 d、3 d、7 d记录15项恢复质量量表(QoR-15)评分及术后住院时间。结果:C组出血量多于B组,差异有统计学意义(P<0.05);A组术后、术后1 d、3 d、5 d的CD3^(+)、CD4^(+)和NK细胞百分比均低于术前,差异有统计学意义(P<0.05),B组术后1 d、3 d、5 d CD3^(+)、CD4^(+)细胞百分比,C组术后3 d CD4^(+)细胞百分比,术后5 d CD3^(+)细胞百分比均高于A组,差异有统计学意义(P<0.05);B、C两组在术后1 d、3 d QoR-15评分均高于A组,差异有统计学意义(P<0.05),C组在术后1 d、3 d QoR-15评分均低于B组,差异有统计学意义(P<0.05);多因素回归显示B组术后住院时间低于A组,差异有统计学意义(P<0.05),C组术后住院时间与A组比较,差异无统计学意义(P=0.69)。结论:回收式自体输血(IOCS)应用于高龄骨科手术患者,相对于异体输血可有效促进患者术后转归,提高术后早期恢复质量,缩短术后住院时间。展开更多
Nowadays,researchers are frequently confronted with challenges from massive data computing by a number of limitations of computer primary memory.Modal regression(MR)is a good alternative of the mean regression and lik...Nowadays,researchers are frequently confronted with challenges from massive data computing by a number of limitations of computer primary memory.Modal regression(MR)is a good alternative of the mean regression and likelihood based methods,because of its robustness and high efficiency.To this end,the authors extend MR to massive data analysis and propose a computationally and statistically efficient divide and conquer MR method(DC-MR).The major novelty of this method consists of splitting one entire dataset into several blocks,implementing the MR method on data in each block,and deriving final results through combining these regression results via a weighted average,which provides approximate estimates of regression results on the entire dataset.The proposed method significantly reduces the required amount of primary memory,and the resulting estimator is theoretically as efficient as the traditional MR on the entire data set.The authors also investigate a multiple hypothesis testing variable selection approach to select significant parametric components and prove the approach possessing the oracle property.In addition,the authors propose a practical modified modal expectation-maximization(MEM)algorithm for the proposed procedures.Numerical studies on simulated and real datasets are conducted to assess and showcase the practical and effective performance of our proposed methods.展开更多
This paper presents a robust estimation procedure by using modal regression for the partial functional linear regression,which combines the common linear model with the functional linear regression model.The outstandi...This paper presents a robust estimation procedure by using modal regression for the partial functional linear regression,which combines the common linear model with the functional linear regression model.The outstanding merit of the new method is that it is robust against outliers or heavy-tail error distributions while performs no worse than the least-square-based estimation method for normal error cases.The slope function is fitted by B-spline.Under suitable conditions,the authors obtain the convergence rates and asymptotic normality of the estimators.Finally,simulation studies and a real data example are conducted to examine the finite sample performance of the proposed method.Both the simulation results and the real data analysis confirm that the newly proposed method works very well.展开更多
文摘In this paper, we investigate the empirical likelihood diagnosis of modal linear regression models. The empirical likelihood ratio function based on modal regression estimation method for the regression coefficient is introduced. First, the estimation equation based on empirical likelihood method is established. Then, some diagnostic statistics are proposed. At last, we also examine the performance of proposed method for finite sample sizes through simulation study.
文摘针对滑坡位移难以预测、影响因素难以选择等问题,提出一种结合了二次移动平均(DMA)法、变分模态分解(VMD)、改进灰狼优化(IGWO)算法与支持向量回归(SVR)的模型进行滑坡位移预测。首先,利用DMA提取滑坡位移趋势项和周期项,采用多项式拟合对趋势项进行预测;其次,对滑坡周期项的影响因素进行分类,采用VMD对原始影响因子序列进行分解获得最优序列;再次,提出一种结合SVR与基于改进Circle多策略的灰狼优化算法CTGWO-SVR(Circle Tactics Grey Wolf Optimizer with SVR)对滑坡周期项进行预测;最后采用时间序列加法模型求出累计位移预测序列,并采用灰色预测的后验证差校验和小概率误差对模型进行评价。实验结果表明,与GA-SVR和GWO-SVR模型相比,CTGWO-SVR的预测精度更高,拟合度达到0.979,均方根误差分别减小了51.47%与59.25%,预测精度等级为一级,可满足滑坡预测的实时性和准确性要求。
文摘目的:观察不同输血方式对高龄骨科手术患者术后转归的影响。方法:将90例择期骨科手术患者根据术中不同输血模式分为异体输血组(A组)和自体输血组(B组),自体+异体输血组(C组),每组各30例。患者全身麻醉后,桡动脉采血检测Hb、Hct值;观察并记录术中失血量、异体输血量、自体输血量、尿量、手术时间及液体输入量;分别于术前、术后、术后1 d、3 d、5 d采集静脉血用流式细胞仪检测CD3^(+)、CD4^(+)及NK细胞百分比;术前1 d、术后1 d、3 d、7 d记录15项恢复质量量表(QoR-15)评分及术后住院时间。结果:C组出血量多于B组,差异有统计学意义(P<0.05);A组术后、术后1 d、3 d、5 d的CD3^(+)、CD4^(+)和NK细胞百分比均低于术前,差异有统计学意义(P<0.05),B组术后1 d、3 d、5 d CD3^(+)、CD4^(+)细胞百分比,C组术后3 d CD4^(+)细胞百分比,术后5 d CD3^(+)细胞百分比均高于A组,差异有统计学意义(P<0.05);B、C两组在术后1 d、3 d QoR-15评分均高于A组,差异有统计学意义(P<0.05),C组在术后1 d、3 d QoR-15评分均低于B组,差异有统计学意义(P<0.05);多因素回归显示B组术后住院时间低于A组,差异有统计学意义(P<0.05),C组术后住院时间与A组比较,差异无统计学意义(P=0.69)。结论:回收式自体输血(IOCS)应用于高龄骨科手术患者,相对于异体输血可有效促进患者术后转归,提高术后早期恢复质量,缩短术后住院时间。
基金supported by the Fundamental Research Funds for the Central Universities under Grant No.JBK1806002the National Natural Science Foundation of China under Grant No.11471264。
文摘Nowadays,researchers are frequently confronted with challenges from massive data computing by a number of limitations of computer primary memory.Modal regression(MR)is a good alternative of the mean regression and likelihood based methods,because of its robustness and high efficiency.To this end,the authors extend MR to massive data analysis and propose a computationally and statistically efficient divide and conquer MR method(DC-MR).The major novelty of this method consists of splitting one entire dataset into several blocks,implementing the MR method on data in each block,and deriving final results through combining these regression results via a weighted average,which provides approximate estimates of regression results on the entire dataset.The proposed method significantly reduces the required amount of primary memory,and the resulting estimator is theoretically as efficient as the traditional MR on the entire data set.The authors also investigate a multiple hypothesis testing variable selection approach to select significant parametric components and prove the approach possessing the oracle property.In addition,the authors propose a practical modified modal expectation-maximization(MEM)algorithm for the proposed procedures.Numerical studies on simulated and real datasets are conducted to assess and showcase the practical and effective performance of our proposed methods.
基金supported by the National Natural Science Foundation of China under Grant Nos.11671096,11690013,11731011。
文摘This paper presents a robust estimation procedure by using modal regression for the partial functional linear regression,which combines the common linear model with the functional linear regression model.The outstanding merit of the new method is that it is robust against outliers or heavy-tail error distributions while performs no worse than the least-square-based estimation method for normal error cases.The slope function is fitted by B-spline.Under suitable conditions,the authors obtain the convergence rates and asymptotic normality of the estimators.Finally,simulation studies and a real data example are conducted to examine the finite sample performance of the proposed method.Both the simulation results and the real data analysis confirm that the newly proposed method works very well.
