基于R - level模型的小面积估算框架

IF 2.3 4区 计算机科学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
R Journal Pub Date : 2023-09-24 DOI:10.32614/rj-2023-039
Sylvia Harmening, Ann-Kristin Kreutzmann, Sören Schmidt, Nicola Salvati, Timo Schmid
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引用次数: 3

摘要

R包[emdi](https://CRAN.R-project.org/package=emdi)便于使用小面积估计方法对区域分解指标进行估计,并提供了用于模型构建、诊断、呈现和导出结果的工具。包版本1.1.7包括依赖于对微数据的访问的单元级小区域模型。自2.0.0版发布以来,@Fay1979的区域级模型和各种扩展已被添加到包中。这些扩展包括(a)具有反向转换的区域级模型,(b)空间和鲁棒扩展,(c)调整方差估计方法,以及(d)考虑测量误差的区域级模型。实现了相应的均方误差估计来评估不确定性。用户友好的工具,如逐步变量选择,模型诊断,基准测试选项,高质量的地图和结果导出选项,使一个完整的分析过程。该软件包的功能通过基于奥地利地区的综合数据的示例来说明。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Framework for Producing Small Area Estimates Based on Area-Level Models in R
The R package [emdi](https://CRAN.R-project.org/package=emdi) facilitates the estimation of regionally disaggregated indicators using small area estimation methods and provides tools for model building, diagnostics, presenting, and exporting the results. The package version 1.1.7 includes unit-level small area models that rely on access to micro data. The area-level model by @Fay1979 and various extensions have been added to the package since the release of version 2.0.0. These extensions include (a) area-level models with back-transformations, (b) spatial and robust extensions, (c) adjusted variance estimation methods, and (d) area-level models that account for measurement errors. Corresponding mean squared error estimators are implemented for assessing the uncertainty. User-friendly tools like a stepwise variable selection, model diagnostics, benchmarking options, high quality maps and results exportation options enable a complete analysis procedure. The functionality of the package is illustrated by examples based on synthetic data for Austrian districts.
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来源期刊
R Journal
R Journal COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-STATISTICS & PROBABILITY
CiteScore
2.70
自引率
0.00%
发文量
40
审稿时长
>12 weeks
期刊介绍: The R Journal is the open access, refereed journal of the R project for statistical computing. It features short to medium length articles covering topics that should be of interest to users or developers of R. The R Journal intends to reach a wide audience and have a thorough review process. Papers are expected to be reasonably short, clearly written, not too technical, and of course focused on R. Authors of refereed articles should take care to: - put their contribution in context, in particular discuss related R functions or packages; - explain the motivation for their contribution; - provide code examples that are reproducible.
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