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

基本信息

  • 批准号:
    2316353
  • 负责人:
  • 金额:
    $ 21.46万
  • 依托单位:
  • 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

项目摘要

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.
本研究项目将开发基于模型的估计器,用于估计小区域的人口参数。小区域估计是抽样调查中的一个重要问题,当样本容量不足以在小区域或区域内提供可靠的估计时。在抽样调查中,为了提高估计量的精度,基于辅助变量的模型估计量被广泛应用。然而,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不能准确地描述这种关系。该项目将开发新的基于模型的估算器,其基础是更灵活的回归模型,称为聚类系数回归模型。新的估计器将应用于不同的国家调查,如美国人口普查局进行的美国社区调查。该项目的结果将作为抽样调查课程的范例。本科生和研究生都将参与这项研究项目。该研究项目将开发基于聚类系数回归模型的新的基于模型的估计器,该模型使用惩罚函数,也可以借用不同区域的空间或排序信息。该项目将弥合聚类系数回归和基于模型的估计之间的差距。本文将研究小区域估计中的三种类型的估计量。首先,该项目将为线性回归模型和逻辑回归模型开发基于聚类系数的新的广义回归估计。第二,该项目将研究基于聚集系数回归模型的新的单位级估计,包括扩展当前的线性模型和考虑随机效应的二进制数据的新估计的发展。最后,该项目将开发基于聚类系数的新的区域水平估算器,包括基于beta回归模型的比例扩展。该奖项反映了NSF的法定使命,并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

项目成果

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Xin Wang其他文献

Distinct contribution of PD-L1 suppression by spatial expression of PD-L1 on tumor and non-tumor cells
PD-L1 在肿瘤和非肿瘤细胞上的空间表达对 PD-L1 抑制的独特贡献
  • DOI:
    10.1038/s41423-018-0021-3
  • 发表时间:
    2018-03
  • 期刊:
  • 影响因子:
    24.1
  • 作者:
    Xiaoqing Zhang;Chen Cheng;Jiyan Hou;Xinyue Qi;Xin Wang;Ping Han;Xuanming Yang
  • 通讯作者:
    Xuanming Yang
Atomically dispersed Sn modified with trace sulfur species derived from organosulfide complex for electroreduction of CO2
用源自有机硫化物络合物的痕量硫物质改性的原子分散锡,用于 CO2 的电还原
  • DOI:
    10.1016/j.apcatb.2021.120936
  • 发表时间:
    2021-11
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Xin Wang;Fengli Li;Wen-Jin Yin;Yubing Si;Ming Miao;Xiaoming Wang;Yongzhu Fu
  • 通讯作者:
    Yongzhu Fu
CALL FOR PAPERS Real-Time Visualization of Lung Function: From Micro to Macro Mechanical ventilation causes airway distension with proinflammatory sequelae in mice
肺功能实时可视化:从微观到宏观机械通气导致小鼠气道扩张并产生促炎后遗症
  • DOI:
  • 发表时间:
    2014
  • 期刊:
  • 影响因子:
    0
  • 作者:
    Hannah T. Nickles;M. Šumkauskaitė;Xin Wang;I. Wegner;M. Puderbach;W. Kuebler
  • 通讯作者:
    W. Kuebler
Nitric acid pressure leaching of limonitic laterite ores: Regeneration of HNO3 and simultaneous synthesis of fibrous CaSO4·2H2O by-products
褐铁矿红土矿硝酸加压浸出:再生HNO3并同时合成纤维状CaSO4·2H2O副产物
  • DOI:
    10.1007/s11771-020-4463-2
  • 发表时间:
    2020-09
  • 期刊:
  • 影响因子:
    4.4
  • 作者:
    Shuang Shao;Bao-zhong Ma;Xin Wang;Wen-juan Zhang;Yong-qiang Chen;Cheng-yan Wang
  • 通讯作者:
    Cheng-yan Wang
Exploiting time-varying graphs for data forwarding in mobile social Delay-Tolerant Networks
利用时变图在移动社交延迟容忍网络中进行数据转发

Xin Wang的其他文献

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{{ truncateString('Xin Wang', 18)}}的其他基金

CAREER: Glycogen metabolism kick-starts photosynthesis in cyanobacteria
事业:糖原代谢启动蓝细菌的光合作用
  • 批准号:
    2414925
  • 财政年份:
    2023
  • 资助金额:
    $ 21.46万
  • 项目类别:
    Continuing Grant
CAREER: Glycogen metabolism kick-starts photosynthesis in cyanobacteria
事业:糖原代谢启动蓝细菌的光合作用
  • 批准号:
    2042182
  • 财政年份:
    2021
  • 资助金额:
    $ 21.46万
  • 项目类别:
    Continuing Grant
Collaborative Research: SWIFT: LARGE: MAC-on-MAC: A Spectrum Orchestrating Control Plane for Coexisting Wireless Systems
合作研究:SWIFT:LARGE:MAC-on-MAC:共存无线系统的频谱编排控制平面
  • 批准号:
    2030063
  • 财政年份:
    2021
  • 资助金额:
    $ 21.46万
  • 项目类别:
    Standard Grant
CIF: Small: Improving Sensing and Estimation with Co-array Techniques
CIF:小型:利用联合阵列技术改进传感和估计
  • 批准号:
    2007313
  • 财政年份:
    2020
  • 资助金额:
    $ 21.46万
  • 项目类别:
    Standard Grant
SpecEES: Collaborative Research: Spatially Oversampled Dense Multi-Beam Millimeter-Wave Communications for Exponentially Increased Energy-Efficiency
SpecEES:协作研究:空间过采样密集多波束毫米波通信,以指数方式提高能源效率
  • 批准号:
    1731238
  • 财政年份:
    2017
  • 资助金额:
    $ 21.46万
  • 项目类别:
    Standard Grant
CRISP Type 2/Collaborative Research: Harnessing Interdependency for Resilience: Creating an "Energy Sponge" with Cloud Electric Vehicle Sharing
CRISP 类型 2/合作研究:利用相互依赖性实现弹性:通过云电动汽车共享创建“能源海绵”
  • 批准号:
    1637772
  • 财政年份:
    2016
  • 资助金额:
    $ 21.46万
  • 项目类别:
    Standard Grant
NeTS: Small: Fundamental Techniques for Incentive-aware, Efficient, and Reliable Cloudlet Management and Services
NetS:小型:激励感知、高效且可靠的 Cloudlet 管理和服务的基本技术
  • 批准号:
    1526843
  • 财政年份:
    2015
  • 资助金额:
    $ 21.46万
  • 项目类别:
    Standard Grant
Collaborative Research: Electronically-Scanned Wideband Digital Aperture Antenna Arrays using Multi-Dimensional Space-Time Circuit-Network Resonance: Theory and Hardware
合作研究:使用多维时空电路网络谐振的电子扫描宽带数字孔径天线阵列:理论和硬件
  • 批准号:
    1408247
  • 财政年份:
    2014
  • 资助金额:
    $ 21.46万
  • 项目类别:
    Standard Grant
Collaborative Research: EARS: Cognitive and Efficient Spectrum Access in Autonomous Wireless Networks
合作研究:EARS:自主无线网络中的认知和高效频谱访问
  • 批准号:
    1247924
  • 财政年份:
    2013
  • 资助金额:
    $ 21.46万
  • 项目类别:
    Standard Grant
NEDG: A Universal Approach to Channel-Adaptive Resource Allocation and Scheduling for Wireless OFDM Networks
NEDG:无线 OFDM 网络信道自适应资源分配和调度的通用方法
  • 批准号:
    0831671
  • 财政年份:
    2008
  • 资助金额:
    $ 21.46万
  • 项目类别:
    Standard Grant

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