Akkaya Hocagil Tugba, Yucel Recai M
Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, Canada.
Department of Epidemiology and Biostatistics, Temple University, Philadelphia, PA, USA.
J Appl Stat. 2023 Nov 6;51(11):2258-2278. doi: 10.1080/02664763.2023.2277669. eCollection 2024.
Due to the computational burden, especially in high-dimensional settings, sequential imputation may not be practical. In this paper, we adopt computationally advantageous methods by sampling the missing data from their perspective predictive distributions, which leads to significantly improved computation time in the class of variable-by-variable imputation algorithms. We assess the computational performance in a comprehensive simulation study. We then compare and contrast the performance of our algorithm with commonly used alternatives. The results show that our method has a significant advantage over the commonly used alternatives with respect to computational efficiency and inferential quality. Finally, we demonstrate our methods in a substantive problem aimed at investigating the effects of area-level behavioral, socioeconomic, and demographic characteristics on poor birth outcomes in New York State among singleton births.
由于计算负担，特别是在高维情况下，顺序插补可能不切实际。在本文中，我们通过从其预测分布中对缺失数据进行采样来采用计算上更具优势的方法，这使得逐个变量插补算法类别中的计算时间显著缩短。我们在一项全面的模拟研究中评估计算性能。然后，我们将我们算法的性能与常用的替代方法进行比较和对比。结果表明，在计算效率和推理质量方面，我们的方法比常用的替代方法具有显著优势。最后，我们在一个实质性问题中展示了我们的方法，该问题旨在研究纽约州单胎出生中地区层面的行为、社会经济和人口特征对不良出生结局的影响。
