项目名称: 抽样调查中的小域估计方法研究
项目编号: No.11301514
项目类型: 青年科学基金项目
立项/批准年度: 2014
项目学科: 数理科学和化学
项目作者: 朱荣
作者单位: 中国科学院数学与系统科学研究院
项目金额: 22万元
中文摘要: 在抽样调查中,小域估计是一个很重要也很棘手的问题,受到越来越多的抽样调查理论和实际工作者的重视,它已经成为抽样调查甚至统计学的热点研究领域。本项目将对小域估计中的若干重要问题进行深入研究,包括:(1)研究模型辅助而非基于模型的小域估计方法;(2)给出在抽样设计意义下小域估计的精度估计;(3)研究小域估计中的区别估计问题;(4)研究一般小域模型下的惩罚加权最小二乘方法及其惩罚参数的选取;(5)将农业的卫星遥感数据和实际调查数据相结合,研究小域估计在农业调查中的实际应用;(6)利用小域估计方法建立我国人口分区域的推算模型。本项目将进一步推动小域估计的理论与方法研究,将对解决我国抽样调查工作中的实际问题提供有力的新工具。
中文关键词: 抽样调查;小区域估计;大规模数据;快速算法;
英文摘要: Small area estimation is a very important and difficult problem in survey sampling. It has been paid more and more attention by the researchers or statistican in survey sampling, and has been a hot topic in survey sampling or statistics. This project is designed to study the small area estimion in several important directions: Part 1 consider how to get the model-assisted rather than model-based small area estimaion; Part 2 focus on measuring the performance of small area estiamtors under the sampling design framework; Part 3 consider the adaptive small area estimation for areas with area-effects and without area-effects by LASSO method; Part 4 investigate the peanlized weighted least square method in general small area models and the choice of the penalty parameters for this method; Part 5 is an application research on agricultural survey, which we would make use of data from the satellite remote and survey sample data; Part 6 consider the effective estimation for demographic areas in our country. This project would extend the method research in small area estimation, and provide the application case for the practical statistican in our country.
英文关键词: Sampling;Small area estimation;large-scale data;fast algorithm;