Statistical Analysis with Computerized Linked Data

使用计算机关联数据进行统计分析

• 批准号: 1758808
• 负责人: Parthasarathi Lahiri
• 金额: $ 30万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2022-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1758808&HistoricalAwards=false
• 关键词: Statistical; Analysis; Computerized; Linked; Data

This research project will develop a new theoretical framework for analyzing data obtained from multiple sources to solve important small area estimation problems. It would be prohibitively expensive to produce statistics on spatial and temporal granularity using traditional methods that rely solely on sample survey data. Large databases such as administrative or census records in combination with sample surveys offer potential solutions to these sparse data problems, but methodological questions arise when these alternative data sources are brought together to produce small area statistics. This project will address these methodological questions by developing a framework that extracts the maximum possible information from multiple sources of data. The project will enable formulation and analysis of complex and challenging questions of scientific interest and societal importance. The research results will impact the definition of new data structures and models and software tools and strategies for analyzing complex computerized data settings, such as social statistics and business. Graduate student research will be supported by this project. To further the dissemination of research results and support the training needs of current and future survey statisticians, the investigator will offer workshops/seminars/webinars in the Washington, D.C. area.Government agencies need timely and finer-grain data to effectively plan and evaluate different government programs for the public good. People generally are more concerned about statistics for their own community, such as the crime rate in their neighborhood last week, than statistics for the entire nation in the last year. The investigator will develop a new general integrated model that combines a permutation linkage model with a small area model to extract maximum possible information from multiple sources of data available at different hierarchical levels. The model will be implemented using a frequentist approach. The investigator will explore the theoretical properties of empirical best predictors and the jackknife estimator of uncertainly under the integrated model. The methodology to be developed will be evaluated by extensive simulations and real data applications. During this project, the investigator will address a number of challenging issues in the presence of certain linkage errors, including modelling, model diagnostics and model selection, measuring uncertainty of the proposed estimators, and evaluation.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.

该研究项目将开发一个新的理论框架，用于分析从多个来源获得的数据，以解决重要的小区域估计问题。使用仅依赖于抽样调查数据的传统方法来编制关于空间和时间粒度的统计数据，其成本将高得令人望而却步。行政或普查记录等大型数据库与抽样调查相结合，为这些数据稀少的问题提供了潜在的解决办法，但当这些替代数据来源被汇集在一起以产生小范围的统计数据时，就会出现方法问题。本项目将通过制定一个框架，从多种数据来源中尽可能提取信息，解决这些方法问题。该项目将有助于制定和分析具有科学意义和社会重要性的复杂和挑战性问题。研究结果将影响新的数据结构和模型的定义，以及用于分析复杂的计算机化数据设置（如社会统计和商业）的软件工具和策略。研究生的研究将得到该项目的支持。为了进一步传播研究成果并支持当前和未来调查统计人员的培训需求，调查员将在华盛顿，华盛顿特区地区举办讲习班/研讨会/网络研讨会。政府机构需要及时和更精细的数据，以有效地规划和评估不同的政府公共利益计划。人们通常更关心自己社区的统计数据，比如上周他们社区的犯罪率，而不是去年全国的统计数据。研究者将开发一种新的通用综合模型，该模型将排列连锁模型与小区域模型相结合，以从不同层次的多个数据来源中提取最大可能的信息。 该模型将使用频率论方法实施。研究者将探讨整合模型下经验最佳预测量与不确定性之折刀估计量之理论性质。 将通过广泛的模拟和真实的数据应用来评价拟开发的方法。在这个项目中，研究人员将解决一些具有挑战性的问题，在存在某些联系的错误，包括建模，模型诊断和模型选择，测量不确定性的建议估计，和evaluation.This奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

Estimation of mask effectiveness perception for small domains using multiple data sources

使用多个数据源估计小域的口罩有效性感知

• DOI: 10.2478/stattrans-2022-0001
• 发表时间: 2022
• 期刊: Statistics in Transition New Series
• 作者: Sen, Aditi;Lahiri, Partha
• 通讯作者: Lahiri, Partha

A general Bayesian approach to meet different inferential goals in poverty research for small areas

满足小地区贫困研究不同推理目标的通用贝叶斯方法

• DOI: 10.21307/stattrans-2020-040
• 发表时间: 2020
• 期刊: Statistics in Transition New Series
• 作者: Lahiri, Partha;Suntornchost, Jiraphan
• 通讯作者: Suntornchost, Jiraphan

Statistical Analysis with Linked Data

• DOI: 10.1111/insr.12295
• 发表时间: 2018-10
• 期刊: International Statistical Review
• 影响因子: 2
• 作者: Ying Han;P. Lahiri

A nested error regression model with high-dimensional parameter for small area estimation

一种用于小区域估计的高维参数嵌套误差回归模型

• DOI: 10.1093/jrsssb/qkac010
• 发表时间: 2023
• 期刊: Journal of the Royal Statistical Society Series B: Statistical Methodology
• 作者: Lahiri, Partha;Salvati, Nicola
• 通讯作者: Salvati, Nicola

Bayesian synthetic prediction of state level poverty using Indian Household Consumer Expenditure Survey Data1

使用印度家庭消费者支出调查数据对州级贫困进行贝叶斯综合预测1

• DOI: 10.3233/sji-220965
• 发表时间: 2022
• 期刊: Statistical Journal of the IAOS
• 作者: Das, Soumojit;Basu, Atanushasan;Lahiri, Partha;Sengupta, Shreya
• 通讯作者: Sengupta, Shreya