Clustered Coefficient Regression Model-Based Estimators in Small Area Estimation

小区域估计中基于聚类系数回归模型的估计器

• 批准号: 2316353
• 负责人: Xin Wang
• 金额: $ 21.46万
• 依托单位: San Diego State University Foundation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2316353&HistoricalAwards=false
• 关键词: Clustered; Coefficient; Regression; Model; Based

This research project will develop model-based estimators for estimating population parameters in small areas. Small area estimation is an important problem in survey sampling when the sample sizes are not large enough to provide reliable estimates in small areas or domains. Model-based estimators based on auxiliary variables are widely used to increase the precision of estimators in survey sampling. However, if a heterogeneous relationship exists between the variable of interest and auxiliary variables, traditional models based on homogeneity will not accurately describe the relationship. This project will develop new model-based estimators based on more flexible regression models called clustered coefficient regression models. The new estimators will be applied to different national surveys, such as American Community Survey conducted by the U.S. Census Bureau. Results from the project will be used as examples in a course on survey sampling. Both undergraduate and graduate students will be involved in the research project. Publicly available R packages also will be developed.This research project will develop new model-based estimators based on clustered coefficient regression models using penalty functions, which also can borrow spatial or ordering information in different areas. The project will bridge the gap between clustered coefficient regression and model-based estimators. Three types of estimators in small area estimation will be studied. First, the project will develop new generalized regression estimators based on clustered coefficients for both linear regression models and logistic regression models. Second, the project will examine new unit level estimators based on clustered coefficient regression models, including an extension of the current linear model and the development of new estimators for binary data with consideration of random effects. Finally, the project will develop new area level estimators based on clustered coefficients, including an extension to proportions based on beta regression models.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该研究项目将开发基于模型的估计器，以估计小区域中的人口参数。当样本量不足以在小区域或域中提供可靠的估计值时，小面积估计是调查采样的重要问题。基于辅助变量的基于模型的估计器被广泛用于提高调查采样中估计器的精度。但是，如果感兴趣的变量和辅助变量之间存在异质关系，则基于同质性的传统模型将无法准确描述这种关系。该项目将基于更灵活的回归模型（称为群集系数回归模型）开发新的基于模型的估计器。新的估计量将应用于不同的国家调查，例如美国人口普查局进行的美国社区调查。该项目的结果将用作调查抽样课程的示例。本科生和研究生都将参与研究项目。该研究项目还将使用惩罚功能基于聚类系数回归模型开发新的基于模型的估计器，该项目还可以在不同领域借入空间或订购信息。该项目将弥合聚类系数回归与基于模型的估计器之间的差距。将研究小面积估计中的三种类型的估计器。首先，该项目将基于线性回归模型和逻辑回归模型的聚类系数开发新的广义回归估计器。其次，该项目将基于聚类系数回归模型检查新的单位级估计器，包括当前线性模型的扩展以及考虑随机效应的新估计器的开发。最后，该项目将根据集群系数开发新的区域级别估计器，包括基于Beta回归模型的比例扩展。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛的影响评估的评估来支持的。标准。