We discuss Bayesian log-linear models for incomplete contingency tables with both missing and interval censored cells, with the aim of obtaining reliable population size estimates. We also discuss use of external information on the censoring probability, which may substantially reduce uncertainty. We show in simulation that information on lower bounds and external information can each improve the mean squared error of population size estimates, even when the external information is not completely accurate. We conclude with an original example on estimation of prevalence of multiple sclerosis in the metropolitan area of Rome, where five out of six lists have interval censored counts. External information comes from mortality rates of multiple sclerosis patients.

中文翻译：

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使用区间删失计数和外部信息估计人口规模：罗马多发性硬化症的患病率

我们讨论了具有缺失和区间删失单元的不完整列联表的贝叶斯对数线性模型，目的是获得可靠的人口规模估计。我们还讨论了关于审查概率的外部信息的使用，这可能会大大降低不确定性。我们在模拟中表明，即使外部信息不完全准确，下界信息和外部信息也可以各自改善人口规模估计的均方误差。我们以一个关于估计罗马大都市区多发性硬化症患病率的原始示例作为结论，其中六分之五的列表具有间隔删失计数。外部信息来自多发性硬化症患者的死亡率。