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
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.
文摘目的 在登革热疾病进展的早期阶段通过建立预测模型来评估重症登革热及登革热伴预警出现的风险,以避免重症登革热在疾病发展早期得不到有效防治,并降低登革热死亡率。方法 回顾性收集云南省瑞丽市人民医院2019—2023年临床及实验室检查等831例患者资料,按7∶3比例分为训练集和验证集。训练集进行统计描述、单因素分析,LASSO回归筛选变量,logistic回归开发登革热重症风险预警模型;训练集和验证集进行ROC曲线模型性能验证。结果 本研究共纳入831名登革热患者,年龄为(44.20±15.02)岁,52.59%为男性,5.42%为缅甸籍;发生重症登革热或登革热伴预警122例,占比14.68%,女性为主(58.20%)。训练集采用LASSO回归筛选发生重症登革热或登革热伴预警的相关变量11个:年龄、头晕、呕吐、凝血酶原时间、部分活化凝血活酶时间、红细胞压积、血小板、单核细胞百分比、单核细胞绝对值、血红蛋白、C反应蛋白(λmin=0.011 59);logistic回归建立重症登革热及登革热伴预警模型,具有统计学意义的变量为年龄[OR=1.034(95%CI:1.016~1.053)]、红细胞压积[OR=1.258(95%CI:1.143~1.519)]、血小板[OR=0.991(95%CI:0.985~0.997)]、血红蛋白[OR=0.919(95%CI:0.873~0.950)]、C反应蛋白[OR=1.019(95%CI:1.004~1.034)]。训练集ROC曲线下面积(area under the curve, AUC)值为0.894(95%CI:0.796~0.867),验证集中AUC值为0.862(95%CI:0.709~0.827)。最佳阈值点(Cut-off)取0.197,灵敏度为0.850,特异度为0.743。结论 本研究建立了LASSO-logistic回归风险预测模型,可在登革热患者患病早期预测发生重症登革热及登革热伴预警风险,提高医院重症登革热的防治能力,有助于指导临床治疗决策。
