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首页> 美国政府科技报告 >Further Developments in a Hierarchical Bayes Approach to Small Area Estimation of Health Insurance Coverage: State-Level Estimates for Demographic Groups
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Further Developments in a Hierarchical Bayes Approach to Small Area Estimation of Health Insurance Coverage: State-Level Estimates for Demographic Groups

机译：用于小区域估算健康保险的等级贝叶斯方法的进一步发展：人口群体的州级估计

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Fisher et al. (2006) developed a hierarchical Bayes model to estimate the number of people without health insurance within demographic groups for states. The Centers for Disease Control and Prevention are interested in estimates of women without health insurance by demographic groups in families that earn less than 200% of the poverty line. Our approach jointly models direct estimates from the Annual Social and Economic Supplement to the Current Population Survey (CPSASEC), and Census 2000 Sample Data, tax, food stamp, and Medicaid data, using a multivariate, hierarchical approach. We have improved the preliminary model in Fisher et al. (2006) by adding census data, improving the mean and variance models for the direct estimates and the administrative records data, and developing a raking procedure. In addition, for variance estimation, we have developed a method that takes into account the variance of the direct estimates that are used in the raking procedure.

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