On linkage bias‐correction for estimators using iterated bootstraps
提出利用自助法技术对概率记录链接中的链接偏差进行校正的估计量，并引入检验判断增加自助迭代次数能否有效减少偏差或仅增加方差。
Abstract By amalgamating data from disparate sources, the resulting integrated dataset becomes a valuable resource for statistical analysis. In probabilistic record linkage, the effectiveness of such integration relies on the availability of linkage variables free from errors. Where this is lacking, the linked data set would suffer from linkage errors and the resultant analyses, linkage bias. This paper proposes a methodology leveraging the bootstrap technique to devise linkage bias‐corrected estimators. Additionally, it introduces a test to assess whether increasing the number of bootstrap iterations meaningfully reduces linkage bias or merely inflates variance without further improving accuracy. An application of these methodologies is demonstrated through the analysis of a simulated dataset featuring hormone information, along with a dataset obtained from linking two data sets from the Australian Bureau of Statistics' labour mobility surveys.