加性分位数回归为非线性关系的建模提供一种灵活、鲁棒的方法.拟合加性分位数模型的方法通常使用样条函数逼近分量,但需要先验的选择节点,计算速度较慢,并不适合大规模数据问题.因此文中提出基于融合Lasso的非参数加性分位数回归模型(No...加性分位数回归为非线性关系的建模提供一种灵活、鲁棒的方法.拟合加性分位数模型的方法通常使用样条函数逼近分量,但需要先验的选择节点,计算速度较慢,并不适合大规模数据问题.因此文中提出基于融合Lasso的非参数加性分位数回归模型(Nonparametric Additive Quantile Regression Model Based on Fused Lasso,AQFL),是在融合Lasso罚和l_(2)罚之间折衷的可对加性分位数回归模型进行估计和变量选择的模型.融合Lasso罚使模型能快速计算,并在局部进行自适应,从而实现对所需分位数甚至极端分位数的预测.同时结合l_(2)罚,在高维数据中将对响应影响较小的协变量函数值压缩为零,实现变量的选择.此外,文中给出保证收敛到全局最优的块坐标ADMM算法(Block Coordinate Alternating Direction Method of Multipliers,BC-ADMM),证明AQFL的预测一致性.在合成数据和碎猪肉数据上的实验表明AQFL在预测准确性和鲁棒性等方面较优.展开更多
[目的]探索并建立一个中风痰瘀滞络证的临床预测诊断模型,可应用于互联网医疗等现代大环境。[方法]对2021年6月至2022年2月上海市浦东新区浦南医院中医科收治的新发脑卒中有偏瘫后遗症的患者305例进行病史采集,给予中医证型诊断。将病例...[目的]探索并建立一个中风痰瘀滞络证的临床预测诊断模型,可应用于互联网医疗等现代大环境。[方法]对2021年6月至2022年2月上海市浦东新区浦南医院中医科收治的新发脑卒中有偏瘫后遗症的患者305例进行病史采集,给予中医证型诊断。将病例以7:3的比例区分为训练集及验证集,运用R语言进行LASSO回归筛选疾病研究因子,以二元Logistic回归建立模型,并予区分度、校准度验证,以列线图形式展示模型。[结果]30个研究因子经过筛选后留下9个因子建立模型,模型区分度:训练集曲线下面积(area under curve,AUC)0.942,95%可信区间(0.906,0.979);验证集AUC 0.951,95%可信区间(0.895,1.000)。校准度:Hosmer-Lemeshow指数(H-L)训练集P=0.47,验证集P=0.39。模型以列线图进行可视化展示。[结论]该诊断模型有较好的诊断效能,可在多种无法进行望诊、切诊的情况下给予辨证参考。展开更多
To overcome the deficiency of traditional mathematical statistics methods,an adaptive Lasso grey model algorithm for regional FDI(foreign direct investment)prediction is proposed in this paper,and its validity is anal...To overcome the deficiency of traditional mathematical statistics methods,an adaptive Lasso grey model algorithm for regional FDI(foreign direct investment)prediction is proposed in this paper,and its validity is analyzed.Firstly,the characteristics of the FDI data in six provinces of Central China are generalized,and the mixture model’s constituent variables of the Lasso grey problem as well as the grey model are defined.Next,based on the influencing factors of regional FDI statistics(mean values of regional FDI and median values of regional FDI),an adaptive Lasso grey model algorithm for regional FDI was established.Then,an application test in Central China is taken as a case study to illustrate the feasibility of the adaptive Lasso grey model algorithm in regional FDI prediction.We also select RMSE(root mean square error)and MAE(mean absolute error)to demonstrate the convergence and the validity of the algorithm.Finally,we train this proposedal gorithm according to the regional FDI statistical data in six provinces in Central China from 2006 to 2018.We then use it to predict the regional FDI statistical data from 2019 to 2023 and show its changing tendency.The extended work for the adaptive Lasso grey model algorithm and its procedure to other regional economic fields is also discussed.展开更多
文摘加性分位数回归为非线性关系的建模提供一种灵活、鲁棒的方法.拟合加性分位数模型的方法通常使用样条函数逼近分量,但需要先验的选择节点,计算速度较慢,并不适合大规模数据问题.因此文中提出基于融合Lasso的非参数加性分位数回归模型(Nonparametric Additive Quantile Regression Model Based on Fused Lasso,AQFL),是在融合Lasso罚和l_(2)罚之间折衷的可对加性分位数回归模型进行估计和变量选择的模型.融合Lasso罚使模型能快速计算,并在局部进行自适应,从而实现对所需分位数甚至极端分位数的预测.同时结合l_(2)罚,在高维数据中将对响应影响较小的协变量函数值压缩为零,实现变量的选择.此外,文中给出保证收敛到全局最优的块坐标ADMM算法(Block Coordinate Alternating Direction Method of Multipliers,BC-ADMM),证明AQFL的预测一致性.在合成数据和碎猪肉数据上的实验表明AQFL在预测准确性和鲁棒性等方面较优.
文摘[目的]探索并建立一个中风痰瘀滞络证的临床预测诊断模型,可应用于互联网医疗等现代大环境。[方法]对2021年6月至2022年2月上海市浦东新区浦南医院中医科收治的新发脑卒中有偏瘫后遗症的患者305例进行病史采集,给予中医证型诊断。将病例以7:3的比例区分为训练集及验证集,运用R语言进行LASSO回归筛选疾病研究因子,以二元Logistic回归建立模型,并予区分度、校准度验证,以列线图形式展示模型。[结果]30个研究因子经过筛选后留下9个因子建立模型,模型区分度:训练集曲线下面积(area under curve,AUC)0.942,95%可信区间(0.906,0.979);验证集AUC 0.951,95%可信区间(0.895,1.000)。校准度:Hosmer-Lemeshow指数(H-L)训练集P=0.47,验证集P=0.39。模型以列线图进行可视化展示。[结论]该诊断模型有较好的诊断效能,可在多种无法进行望诊、切诊的情况下给予辨证参考。
基金This work was supported in part by the National Key R&D Program of China(No.2019YFE0122600),author H.H,https://service.most.gov.cn/in part by the Project of Centre for Innovation Research in Social Governance of Changsha University of Science and Technology(No.2017ZXB07),author J.H,https://www.csust.edu.cn/mksxy/yjjd/shzlcxyjzx.htm+2 种基金in part by the Public Relations Project of Philosophy and Social Science Research Project of the Ministry of Education(No.17JZD022),author J.L,http://www.moe.gov.cn/in part by the Key Scientific Research Projects of Hunan Provincial Department of Education(No.19A015),author J.L,http://jyt.hunan.gov.cn/in part by the Hunan 13th five-year Education Planning Project(No.XJK19CGD011),author J.H,http://ghkt.hntky.com/.
文摘To overcome the deficiency of traditional mathematical statistics methods,an adaptive Lasso grey model algorithm for regional FDI(foreign direct investment)prediction is proposed in this paper,and its validity is analyzed.Firstly,the characteristics of the FDI data in six provinces of Central China are generalized,and the mixture model’s constituent variables of the Lasso grey problem as well as the grey model are defined.Next,based on the influencing factors of regional FDI statistics(mean values of regional FDI and median values of regional FDI),an adaptive Lasso grey model algorithm for regional FDI was established.Then,an application test in Central China is taken as a case study to illustrate the feasibility of the adaptive Lasso grey model algorithm in regional FDI prediction.We also select RMSE(root mean square error)and MAE(mean absolute error)to demonstrate the convergence and the validity of the algorithm.Finally,we train this proposedal gorithm according to the regional FDI statistical data in six provinces in Central China from 2006 to 2018.We then use it to predict the regional FDI statistical data from 2019 to 2023 and show its changing tendency.The extended work for the adaptive Lasso grey model algorithm and its procedure to other regional economic fields is also discussed.
