在假设驾驶人对不同来源交通信息的参考程度不同的情况下,研究驾驶人在先进的出行者交通信息服务系统(advanced traveler information systems,ATIS)提供的多源实时信息条件下的出行途中路径选择行为,针对传统logit模型不能模拟影响变...在假设驾驶人对不同来源交通信息的参考程度不同的情况下,研究驾驶人在先进的出行者交通信息服务系统(advanced traveler information systems,ATIS)提供的多源实时信息条件下的出行途中路径选择行为,针对传统logit模型不能模拟影响变量对目标变量的非线性影响的缺点,利用BP(back propagation)神经网络模型进行信息参考行为的影响因素筛选,通过二元logit模型分析交通信息的参考概率,且作为自变量建立驾驶人出行途中路径选择模型.以南京驾驶人在广播和可变信息板(variable message signs,VMS)两种信息发布源条件下的出行路径选择行为进行模型验证,发现年龄和学历是影响广播信息参考的主要因素,年收入和对本地道路的熟悉程度是影响VMS信息参考的主要因素,驾驶人在接收到外界信息时改变出行路径的概率较大,在未接收到外界信息时选择原路径概率较大.展开更多
Panel count data are frequently encountered when study subjects are under discrete observations.However,limited literature has been found on variable selection for panel count data.In this paper,without considering th...Panel count data are frequently encountered when study subjects are under discrete observations.However,limited literature has been found on variable selection for panel count data.In this paper,without considering the model assumption of observation process,a more general semiparametric transformation model for panel count data with informative observation process is developed.A penalized estimation procedure based on the quantile regression function is proposed for variable selection and parameter estimation simultaneously.The consistency and oracle properties of the estimators are established under some mild conditions.Some simulations and an application are reported to evaluate the proposed approach.展开更多
基金partially supported by the National Natural Science Foundation of China under Grant No.12001485the National Bureau of Statistics of China under Grant No.2020LY073the First Class Discipline of Zhejiang-A(Zhejiang University of Finance and Economics-Statistics)under Grant No.Z0111119010/024。
文摘Panel count data are frequently encountered when study subjects are under discrete observations.However,limited literature has been found on variable selection for panel count data.In this paper,without considering the model assumption of observation process,a more general semiparametric transformation model for panel count data with informative observation process is developed.A penalized estimation procedure based on the quantile regression function is proposed for variable selection and parameter estimation simultaneously.The consistency and oracle properties of the estimators are established under some mild conditions.Some simulations and an application are reported to evaluate the proposed approach.