论文标题
完全贝叶斯基准测试的计算有效方法
A Computationally Efficient Approach to Fully Bayesian Benchmarking
论文作者
论文摘要
在小面积估计中，有时需要使用基于模型的方法在很少或没有数据的区域中产生估计值。在官方统计数据中，我们通常要求一些小面积估计的总估计与内部一致性目的的国家估算一致。执行该协议被称为基准测试，尽管目前存在执行基准测试的方法，但很少有人适合具有不确定性的非正常结果和基准测试的应用。完全可以在基准限制下获得后验分布，完全贝叶斯的基准测试是一种理论上具有吸引力的方法。但是，现有的实施可能在计算上是过于刺激的。 In this paper, we critically review benchmarking methods in the context of small area estimation in low- and middle-income countries with binary outcomes and uncertain benchmarks, and propose a novel approach in which an unbenchmarked method that produces area-level samples can be combined with a rejection sampler or Metropolis-Hastings algorithm to produce benchmarked posterior distributions in a computationally efficient way.为了说明我们方法的灵活性和效率，我们提供了模拟中现有基准方法的比较，以及对HIV患病率和5岁以下死亡率估计的应用。实施我们方法论的代码可在R软件包Stbench中获得。
In small area estimation, it is sometimes necessary to use model-based methods to produce estimates in areas with little or no data. In official statistics, we often require that some aggregate of small area estimates agree with a national estimate for internal consistency purposes. Enforcing this agreement is referred to as benchmarking, and while methods currently exist to perform benchmarking, few are ideal for applications with non-normal outcomes and benchmarks with uncertainty. Fully Bayesian benchmarking is a theoretically appealing approach insofar as we can obtain posterior distributions conditional on a benchmarking constraint. However, existing implementations may be computationally prohibitive. In this paper, we critically review benchmarking methods in the context of small area estimation in low- and middle-income countries with binary outcomes and uncertain benchmarks, and propose a novel approach in which an unbenchmarked method that produces area-level samples can be combined with a rejection sampler or Metropolis-Hastings algorithm to produce benchmarked posterior distributions in a computationally efficient way. To illustrate the flexibility and efficiency of our approach, we provide comparisons to an existing benchmarking approach in a simulation, and applications to HIV prevalence and under-5 mortality estimation. Code implementing our methodology is available in the R package stbench.
