随着计算机的发展,函数型变量的数据收集变得越来越容易,传统的不等概抽样方法仅考虑了标量型的辅助变量,同时在不等概抽样方法中确定总体单元入样概率并不容易,为此提出了一种带有函数型变量的不等概抽样方法,这种不等概率抽样方法从...随着计算机的发展,函数型变量的数据收集变得越来越容易,传统的不等概抽样方法仅考虑了标量型的辅助变量,同时在不等概抽样方法中确定总体单元入样概率并不容易,为此提出了一种带有函数型变量的不等概抽样方法,这种不等概率抽样方法从样本数据中提取信息构造总体单元入样概率,克服了确定每个总体单元入样概率的困难。模拟结果表明带有函数型变量的不等概抽样估计结果优于简单随机抽样估计结果。最后将其应用到共享单车数据中得到带有函数型变量的不等概抽样的良好表现。With the development of computers, data collection for functional variables has become increasingly easy. Traditional inequality sampling methods only consider scalar auxiliary variables, and it is not easy to determine the sampling probability of population units in inequality sampling methods. Therefore, unequal Probability sampling method with functional variables is proposed. This unequal probability sampling method extracts information from sample data to construct the sampling probability of population units, overcoming the difficulty of determining the sampling probability of each population unit. The simulation results show that the estimation results of unequal probability sampling with functional variables are better than those of simple random sampling estimation. Finally, the method is applied to shared bicycle data and achieves good performance in unequal probability sampling with functional variables.展开更多
文摘随着计算机的发展,函数型变量的数据收集变得越来越容易,传统的不等概抽样方法仅考虑了标量型的辅助变量,同时在不等概抽样方法中确定总体单元入样概率并不容易,为此提出了一种带有函数型变量的不等概抽样方法,这种不等概率抽样方法从样本数据中提取信息构造总体单元入样概率,克服了确定每个总体单元入样概率的困难。模拟结果表明带有函数型变量的不等概抽样估计结果优于简单随机抽样估计结果。最后将其应用到共享单车数据中得到带有函数型变量的不等概抽样的良好表现。With the development of computers, data collection for functional variables has become increasingly easy. Traditional inequality sampling methods only consider scalar auxiliary variables, and it is not easy to determine the sampling probability of population units in inequality sampling methods. Therefore, unequal Probability sampling method with functional variables is proposed. This unequal probability sampling method extracts information from sample data to construct the sampling probability of population units, overcoming the difficulty of determining the sampling probability of each population unit. The simulation results show that the estimation results of unequal probability sampling with functional variables are better than those of simple random sampling estimation. Finally, the method is applied to shared bicycle data and achieves good performance in unequal probability sampling with functional variables.
