Abstract

Nonignorable missing data presents a great challenge in statistical applications, since the observed likelihood is not identifiable without any further restrictions. In this paper, we make inference about the differences between the corresponding parameters of two independent samples with nonignorable missing renponse. To address the identifiability issue, we consider a parametric propensity model and utilize group label information as an instrument. Two-step generalized method of moments is applied to estimate the parameters of the propensity based on the instrumental estimating equations, and then population parameters are estimated based on the inverse probability weighting with the estimated propensity. The asymptotic properties of the resulting estimators are established. The finite-sample performance of the differences for the population means, distribution functions and quantiles is studied through simulations, and an application to Korean Labor and Income Panel Study (KLIPS) data set is also presented.

摘要

不可忽略的缺失数据在统计应用中提出了巨大的挑战，因为没有任何进一步的限制就无法确定观察到的可能性。在本文中，我们推论了两个具有不可忽略的缺失响应的独立样本的相应参数之间的差异。为了解决可识别性问题，我们考虑参数化倾向模型，并使用组标签信息作为工具。采用两步广义矩法，根据工具估计方程对倾向性参数进行估计，然后根据具有估计倾向性的逆概率加权估计总体参数。建立了所得估计量的渐近性质。