KLATASDS - MOE, School of Statistics, East China Normal University, Shanghai, China.
Biometrics. 2023 Jun;79(2):1268-1279. doi: 10.1111/biom.13666. Epub 2022 Apr 7.
Missing data are frequently encountered in various disciplines and can be divided into three categories: missing completely at random (MCAR), missing at random (MAR), and missing not at random (MNAR). Valid statistical approaches to missing data depend crucially on correct identification of the underlying missingness mechanism. Although the problem of testing whether this mechanism is MCAR or MAR has been extensively studied, there has been very little research on testing MAR versus MNAR. A critical challenge that is faced when dealing with this problem is the issue of model identification under MNAR. In this paper, under a logistic model for the missing probability, we develop two score tests for the problem of whether the missingness mechanism is MAR or MNAR under a parametric model and a semiparametric location model on the regression function. The implementation of the score tests circumvents the identification issue as it requires only parameter estimation under the null MAR assumption. Our simulations and analysis of human immunodeficiency virus data show that the score tests have well-controlled type I errors and desirable powers.
在各种学科中，经常会遇到缺失数据，可以将其分为三类：完全随机缺失（MCAR）、随机缺失（MAR）和非随机缺失（MNAR）。缺失数据的有效统计方法在很大程度上取决于正确识别潜在的缺失机制。尽管已经对检验该机制是否为 MCAR 或 MAR 的问题进行了广泛研究，但对于检验 MAR 与 MNAR 的问题却很少有研究。在处理这个问题时，面临的一个关键挑战是在 MNAR 下模型识别的问题。在本文中，对于缺失概率的逻辑模型，我们针对缺失机制是 MAR 还是 MNAR 的问题，在参数模型和回归函数上的半参数位置模型下，发展了两种得分检验。得分检验的实现规避了识别问题，因为它只需要在零假设 MAR 下进行参数估计。我们的模拟和人类免疫缺陷病毒数据的分析表明，得分检验具有良好的控制的Ⅰ型错误和理想的功效。
