Regression analysis with linked data: problems and possible solutions
Regression analysis with linked data: problems and possible solutions
复制标题
使用关联数据进行回归分析：问题和可能的解决方案
DOI：
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发表时间：
2015
期刊：
影响因子：
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通讯作者：
B. Liseo
中科院分区：
文献类型：
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作者：
A. Tancredi;B. Liseo
在本文中，我们已经描述和扩展了最近的一些建议，一般贝叶斯方法进行记录链接和推理使用所得到的匹配单元。特别是，我们已经将记录链接过程框成一个正式的统计模型，该模型包括匹配变量和在推理阶段包括的其他变量。通过这种方式，研究人员能够在基于概率链接数据的推理过程中考虑匹配过程的不确定性，同时，他/她还能够在工作统计模型和记录链接阶段之间生成信息的反馈传播。我们认为，这种反馈效应是必不可少的，以消除潜在的偏见，否则将得到的链接数据推断的特点，并能够提高记录链接性能。该过程的实际实施是基于使用标准贝叶斯计算技术，如马尔可夫链蒙特卡罗算法。虽然该方法是相当普遍的，我们已经限制了我们的分析，以流行的和重要的情况下，多元线性回归设置，为方便起见。
In this paper we have described and extended some recent proposals on a general Bayesian methodology for performing record linkage and making inference using the resulting matched units. In particular, we have framed the record linkage process into a formal statistical model which comprises both the matching variables and the other variables included at the inferential stage. This way, the researcher is able to account for the matching process uncertainty in inferential procedures based on probabilistically linked data, and at the same time, he/she is also able to generate a feedback propagation of the information between the working statistical model and the record linkage stage. We have argued that this feedback effect is both essential to eliminate potential biases that otherwise would characterize the resulting linked data inference, and able to improve record linkage performances. The practical implementation of the procedure is based on the use of standard Bayesian computational techniques, such as Markov Chain Monte Carlo algorithms. Although the methodology is quite general, we have restricted our analysis to the popular and important case of multiple linear regression set-up for expository convenience.