Bayesian Multisource Hierarchical Models with Applications to the Monthly Retail Trade Survey

IF 1.6 4区 数学 Q2 SOCIAL SCIENCES, MATHEMATICAL METHODS
Stephen J Kaputa, Darcy Steeg Morris, S. Holan
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引用次数: 0

Abstract

The integration of multiple survey, administrative, and third-party data offers the opportunity to innovate and improve survey estimation via statistical modeling. With decreasing response rates and increasing interest for more timely and geographically detailed estimates, imputation methodology that combines multiple data sources to adjust for low unit response and allow for more detailed publication levels, including geographic estimates, is both timely and necessary. Motivated by the Advance Monthly Retail Trade Survey (MARTS) and Monthly Retail Trade Survey (MRTS), we propose Bayesian hierarchical multiple imputation-dependent data models with the goals of automating imputation for the MARTS by using historic MRTS data and providing geographically granular (state-level) estimates for the MRTS via mass imputation using third-party data and spatial dependence. As a natural byproduct of this approach, measures of uncertainty are provided. This article illustrates the advantages of applying established Bayesian hierarchical modeling techniques with multiple source data to address practical problems in official statistics and is, therefore, of independent interest. The motivating empirical studies are unified by their hierarchical modeling framework, which ultimately results in a more principled approach for estimation for the MARTS and a more geographically granular data product for the MRTS.
贝叶斯多源层次模型在零售业月度调查中的应用
多种调查、行政和第三方数据的整合为通过统计建模创新和改进调查估算提供了机会。随着响应率的下降以及人们对更及时和更详细的地理估算的兴趣日益增加,结合多种数据源以调整低单位响应并允许更详细的发布水平(包括地理估算)的估算方法既及时又必要。受 "每月零售贸易先期调查"(MARTS)和 "每月零售贸易调查"(MRTS)的启发,我们提出了贝叶斯分层多重估算依赖数据模型,目的是通过使用 "每月零售贸易调查 "的历史数据实现 "每月零售贸易调查 "的自动估算,并通过使用第三方数据和空间依赖性进行大规模估算,为 "每月零售贸易调查 "提供地理粒度(州级)估算。作为这种方法的自然副产品,还提供了不确定性度量。这篇文章说明了将成熟的贝叶斯分层建模技术应用于多源数据以解决官方统计实际问题的优势,因此具有独立的意义。激励性实证研究由其分层建模框架统一起来,最终为 MARTS 提供了一种更加原则性的估算方法,并为 MRTS 提供了一种更具地理粒度的数据产品。
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来源期刊
CiteScore
4.30
自引率
9.50%
发文量
40
期刊介绍: The Journal of Survey Statistics and Methodology, sponsored by AAPOR and the American Statistical Association, began publishing in 2013. Its objective is to publish cutting edge scholarly articles on statistical and methodological issues for sample surveys, censuses, administrative record systems, and other related data. It aims to be the flagship journal for research on survey statistics and methodology. Topics of interest include survey sample design, statistical inference, nonresponse, measurement error, the effects of modes of data collection, paradata and responsive survey design, combining data from multiple sources, record linkage, disclosure limitation, and other issues in survey statistics and methodology. The journal publishes both theoretical and applied papers, provided the theory is motivated by an important applied problem and the applied papers report on research that contributes generalizable knowledge to the field. Review papers are also welcomed. Papers on a broad range of surveys are encouraged, including (but not limited to) surveys concerning business, economics, marketing research, social science, environment, epidemiology, biostatistics and official statistics. The journal has three sections. The Survey Statistics section presents papers on innovative sampling procedures, imputation, weighting, measures of uncertainty, small area inference, new methods of analysis, and other statistical issues related to surveys. The Survey Methodology section presents papers that focus on methodological research, including methodological experiments, methods of data collection and use of paradata. The Applications section contains papers involving innovative applications of methods and providing practical contributions and guidance, and/or significant new findings.
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