论文标题
识别增强的广义线性模型估计，并具有不可贬低的结果
Identification enhanced generalised linear model estimation with nonignorable missing outcomes
论文作者
论文摘要
缺少数据通常会导致不良偏见和效率降低。当响应机制不可降低时，这些问题将变得很大，这意味着响应模型取决于未观察到的变量。为了管理不可偿还的无响应，有必要估计未观察到的变量和响应指标的联合分布。但是，即使对目标关节分布进行仔细的估计，模型错误指定和识别问题也可以防止强大的估计。在这项研究中，我们将观察到的部分的分布建模，并为模型可识别性提供了足够的条件，假设逻辑回归模型是响应机制和广义线性模型作为关注的主要结果模型。更重要的是，派生的足够条件不需要任何工具变量，这些变量通常被认为可以保证模型可识别性，但不能事先确定。为了分析应用程序中缺少的数据，我们提出了实用的准则和灵敏度分析，以确定响应机制。此外，我们介绍了拟议估计量在数值研究中的性能，并将提出的方法应用于两组实际数据：从第19届韩国大选中退出民意调查以及从韩国对家庭财政和生活条件调查中收集的公共数据。
Missing data often result in undesirable bias and loss of efficiency. These issues become substantial when the response mechanism is nonignorable, meaning that the response model depends on unobserved variables. To manage nonignorable nonresponse, it is necessary to estimate the joint distribution of unobserved variables and response indicators. However, model misspecification and identification issues can prevent robust estimates, even with careful estimation of the target joint distribution. In this study, we modeled the distribution of the observed parts and derived sufficient conditions for model identifiability, assuming a logistic regression model as the response mechanism and generalized linear models as the main outcome model of interest. More importantly, the derived sufficient conditions do not require any instrumental variables, which are often assumed to guarantee model identifiability but cannot be practically determined beforehand. To analyze missing data in applications, we propose practical guidelines and sensitivity analysis to determine the response mechanism. Furthermore, we present the performance of the proposed estimators in numerical studies and apply the proposed method to two sets of real data: exit polls from the 19th South Korean election and public data collected from the Korean Survey of Household Finances and Living Conditions.
