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
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
目的构建列线图模型预测重症监护室(intensive care unit,ICU)机械通气患者压力性损伤(pressure injuries,PI)的发生风险。方法回顾性收集2020年1月1日至2023年3月15日复旦大学附属中山医院ICU接受机械通气患者的数据作为训练集,2023年1...目的构建列线图模型预测重症监护室(intensive care unit,ICU)机械通气患者压力性损伤(pressure injuries,PI)的发生风险。方法回顾性收集2020年1月1日至2023年3月15日复旦大学附属中山医院ICU接受机械通气患者的数据作为训练集,2023年10月1日至2023年12月11日同一医院ICU接受机械通气患者的数据作为外部验证集。基于LASSO回归和Cox比例风险模型筛选PI的风险变量,构建列线图模型。绘制受试者工作特征(receiver operating characteristic,ROC)曲线并计算曲线下面积(area under the curve,AUC)以评价模型区分度,绘制校准曲线及决策曲线分析(decision curve analysis,DCA)评价模型校准度和临床适用性。将验证集数据代入列线图模型进行外部验证。结果训练集共纳入580例机械通气患者,84例(14.5%)发生PI。LASSO回归和Cox比例风险模型共筛选10个变量,构建列线图模型。ROC曲线显示,预测机械通气患者发生PI的AUC为0.830。校准曲线和DCA曲线提示模型校准度和预测效能良好。外部验证集共100例患者,12例发生PI,AUC为0.870,校准曲线和DCA曲线显示模型性能良好。结论基于LASSO-Cox回归构建的列线图模型预测性能较好,可用于机械通气患者PI高危人群的筛查。展开更多
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.
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.
文摘目的构建列线图模型预测重症监护室(intensive care unit,ICU)机械通气患者压力性损伤(pressure injuries,PI)的发生风险。方法回顾性收集2020年1月1日至2023年3月15日复旦大学附属中山医院ICU接受机械通气患者的数据作为训练集,2023年10月1日至2023年12月11日同一医院ICU接受机械通气患者的数据作为外部验证集。基于LASSO回归和Cox比例风险模型筛选PI的风险变量,构建列线图模型。绘制受试者工作特征(receiver operating characteristic,ROC)曲线并计算曲线下面积(area under the curve,AUC)以评价模型区分度,绘制校准曲线及决策曲线分析(decision curve analysis,DCA)评价模型校准度和临床适用性。将验证集数据代入列线图模型进行外部验证。结果训练集共纳入580例机械通气患者,84例(14.5%)发生PI。LASSO回归和Cox比例风险模型共筛选10个变量,构建列线图模型。ROC曲线显示,预测机械通气患者发生PI的AUC为0.830。校准曲线和DCA曲线提示模型校准度和预测效能良好。外部验证集共100例患者,12例发生PI,AUC为0.870,校准曲线和DCA曲线显示模型性能良好。结论基于LASSO-Cox回归构建的列线图模型预测性能较好,可用于机械通气患者PI高危人群的筛查。
