面向位置、尺度和形状的广义加性模型的调查鲁棒不确定性量化
Survey-robust uncertainty quantification in generalised additive models for location, scale, and shape
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中文总结 AI 辅助
本文针对GAMLSS现有标准误差无法考虑复杂调查设计的问题，提出纳入调查设计的线性化三明治方差估计量，经模拟和卢旺达DHS数据重分析验证其有效性。
中文摘要 AI 辅助
传统仅含均值的回归模型对于复杂调查数据的分析往往过于局限，在这类分析中，研究兴趣常超出条件均值，延伸至响应分布的其他方面。广义加性模型（GAMLSS）提供了灵活框架，允许所有分布参数依赖于协变量。然而，现有的基于模型和模型鲁棒的标准误差无法考虑复杂调查设计，会大幅低估回归参数估计的变异性。我们提出一种基于线性化的三明治方差估计量，该估计量纳入了调查设计，同时保持计算效率。通过基于合成调查数据的模拟研究，我们证明所提估计量对所有分布参数能提供准确的标准误差估计和可靠的置信区间覆盖率，且相比基于重复抽样的方法是计算高效的替代方案。我们还通过重新分析2019/20年卢旺达人口与健康调查（DHS）的数据，说明了该方法的实际效用。
英文摘要
Conventional mean-only regression models are often too restrictive for the analysis of complex survey data, where interest frequently extends beyond the conditional mean to other aspects of the response distribution. Generalised additive models for location, scale and shape (GAMLSS) provide a flexible framework by allowing all distributional parameters to depend on covariates. However, existing model-based and model-robust standard errors fail to account for complex survey designs and can substantially underestimate the variability of regression parameter estimates. We propose a linearisation-based sandwich variance estimator that incorporates the survey design while remaining computationally efficient. Using a simulation study based on synthetic survey data, we demonstrate that the proposed estimator provides accurate standard error estimates and reliable confidence interval coverage for all distributional parameters, while offering a computationally efficient alternative to replication-based methods. We further illustrate the practical utility of the approach through a re-analysis of data from the 2019/20 Rwanda Demographic and Health Survey (DHS).
