The link between health insurance coverage and citizenship among immigrants: Bayesian unit-level regression modelling of categorical survey data observed with measurement error
针对美国移民公民身份与健康保险覆盖关系的研究，提出加权伪似然混合分类分布模型，处理调查设计和误报导致的偏差，并用CPS数据验证。
Abstract Social scientists are interested in studying the relationship between citizenship status and health insurance coverage among immigrants in the US. This can be done using data from the Current Population Survey (CPS); however, two primary challenges emerge. First, statistical models must account for the survey design in some fashion to reduce the risk of bias due to informative sampling. Second, it has been observed that survey respondents misreport citizenship status at nontrivial rates. This too can induce bias within a statistical model. Thus, we propose the use of a weighted pseudo-likelihood mixture of categorical distributions, where the mixture component is determined by the latent true response variable, in order to model the misreported data. We illustrate through an empirical simulation study that this approach can mitigate the two sources of bias attributable to the sample design and misreporting. Importantly, our misreporting model can be further used as a component in a deeper hierarchical model. With this in mind, we conduct an analysis of the relationship between health insurance coverage and citizenship status using data from the CPS.