Innovations in Statistical Methodology for Complex Surveys

复杂调查统计方法的创新

• 批准号: 1733572
• 负责人: Jae-Kwang Kim
• 金额: $ 43万
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-15 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1733572&HistoricalAwards=false
• 关键词: Innovations; Statistical; Methodology; Complex; Surveys

This research project will modernize survey sampling tools for inference from survey data that can be used for complex problems such as survey integration or small area estimation. Increasingly, data sources have characteristics that traditional sampling tools are not readily designed to handle. The project will address important inference problems, such as model selection and hypothesis testing, based on data from complex samples and will extend the methods from parametric models to more flexible nonparametric models, such as quantile regression. The new methodology will have broad applicability to surveys in diverse disciplines, including agriculture, health, and demographics. Products of this research will include resources to equip practicing survey statisticians with tools to better meet the demands of policy makers and the public. Software and metadata produced by this project will be made publicly available. Graduate students will receive education and training, with an emphasis on the relationships between design-based and model-based inference.This research project will develop innovative methods that will enable survey statisticians to exploit modern data structures and models in a statistically defensible way. The project will focus on three areas: (1) the use of inverse sampling and reweighting to obtain valid inferences from nonprobability samples, (2) methods for prediction and analytic inference under complex sample designs, and (3) hierarchical modeling strategies that are feasible to implement with large, diverse data sources. Methods to obtain approximately unbiased inferences from non-probability samples are highly relevant because of increases in nonresponse and use of non-survey data, such as administrative sources and satellite information. This examination of inference under complex sample designs will further research on hypothesis testing and the use of semiparametric models, particularly for situations in which the sample design is informative for the specified model. The investigators will further develop a hierarchical modeling approach that aggregates estimates obtained for sub-divisions of a large data source. The investigators have vetted this procedure using non-survey data and will apply the approach in a large-scale survey context.

该研究项目将使调查抽样工具现代化，以便从调查数据中进行推断，这些数据可用于调查整合或小面积估计等复杂问题。越来越多的数据源具有传统采样工具无法处理的特性。该项目将解决重要的推理问题，如模型选择和假设检验，根据复杂样本的数据，并将扩大从参数模型的方法，更灵活的非参数模型，如分位数回归。新方法将广泛适用于农业、卫生和人口统计等不同学科的调查。这项研究的成果将包括为从事调查统计工作的人员提供更好地满足决策者和公众需求的工具的资源。该项目制作的软件和元数据将公开提供。研究生将接受教育和培训，重点是基于设计和基于模型的推理之间的关系。本研究项目将开发创新方法，使调查统计学家能够以统计学上可辩护的方式利用现代数据结构和模型。该项目将重点关注三个领域：（1）使用逆抽样和重新加权从非概率样本中获得有效的推断，（2）复杂样本设计下的预测和分析推断方法，以及（3）可用于实施的分层建模策略大规模、多样化的数据源。从非概率样本中获得近似无偏推论的方法是高度相关的，因为无应答和使用非调查数据（如行政来源和卫星信息）的增加。复杂样本设计下的推理检查将进一步研究假设检验和半参数模型的使用，特别是对于样本设计为指定模型提供信息的情况。研究人员将进一步开发一种分层建模方法，该方法聚合了从大型数据源的细分中获得的估计值。调查人员已经使用非调查数据审查了这一程序，并将在大规模调查中应用这一方法。

项目成果

Within-cluster resampling for multilevel models under informative cluster size

信息簇大小下多级模型的簇内重采样

• DOI: 10.1093/biomet/asz035
• 发表时间: 2019
• 期刊: Biometrika
• 影响因子: 2.7
• 作者: Lee, D.;Kim, J. K.;Skinner, C. J.
• 通讯作者: Skinner, C. J.

A note on the equivalence of two semiparametric estimation methods for nonignorable nonresponse

• DOI: 10.1016/j.spl.2018.03.020
• 发表时间: 2018-09
• 期刊: Statistics & Probability Letters
• 影响因子: 0.8
• 作者: Kosuke Morikawa;Jae Kwang Kim

Bottom-up estimation and top-down prediction: Solar energy prediction combining information from multiple sources

自下而上的估计和自上而下的预测：结合多源信息的太阳能预测

• DOI: 10.1214/18-aoas1145
• 发表时间: 2018
• 期刊: The Annals of Applied Statistics
• 作者: Hwang, Youngdeok;Lu, Siyuan;Kim, Jae-Kwang
• 通讯作者: Kim, Jae-Kwang

Prediction of small area quantiles for the conservation effects assessment project using a mixed effects quantile regression model

• DOI: 10.1214/19-aoas1276
• 发表时间: 2019-12
• 期刊: The Annals of Applied Statistics
• 作者: Emily J. Berg;Danhyang Lee

Sampling Techniques for Big Data Analysis

大数据分析的采样技术

• DOI: 10.1111/insr.12290
• 发表时间: 2018
• 期刊: International Statistical Review
• 影响因子: 2
• 作者: Kim, Jae Kwang;Wang, Zhonglei
• 通讯作者: Wang, Zhonglei