Semiparametric inference for estimating equations with nonignorably missing covariates
Semiparametric inference for estimating equations with nonignorably missing covariates
复制标题
用于估计具有不可忽略的缺失协变量的方程的半参数推理
DOI：
10.1080/10485252.2018.1482295
复制
发表时间：
2018
影响因子：
1.2
通讯作者：
Zhiguo Xiao
中科院分区：
文献类型：
--
作者：
Ji Chen;Fang Fang;Zhiguo Xiao
当某些协变量具有不可忽略的缺失值时，我们考虑对估计方程（EE）中的未知参数进行统计推断，这在实践中很常见，但在文献中很少讨论。当有一个工具，一个完全观察到的协变量向量，有助于识别不可忽略缺失下的参数时，可以通过Zhao和Shao的伪似然方法来估计给定其他协变量的缺失协变量的条件分布[(2015)，“具有不可忽略缺失数据的广义线性模型中的半参数伪似然”，美国统计协会杂志，110, 1577–1590)]用于构造无偏 EE。然后，这些修改后的 EE 构成了通过经验可能性进行有效推断的基础。我们的方法适用于实践中使用的各种 EE。它是半参数的，因为没有假设缺失协变量数据的倾向的参数模型。推导了所提出的估计量的渐近性质和经验似然比检验统计量。一些模拟结果和实际数据分析可供说明。
We consider statistical inference of unknown parameters in estimating equations (EEs) when some covariates have nonignorably missing values, which is quite common in practice but has rarely been discussed in the literature. When an instrument, a fully observed covariate vector that helps identifying parameters under nonignorable missingness, is available, the conditional distribution of the missing covariates given other covariates can be estimated by the pseudolikelihood method of Zhao and Shao [(2015), ‘Semiparametric pseudo likelihoods in generalised linear models with nonignorable missing data’,Journal of the American Statistical Association, 110, 1577–1590)] and be used to construct unbiased EEs. These modified EEs then constitute a basis for valid inference by empirical likelihood. Our method is applicable to a wide range of EEs used in practice. It is semiparametric since no parametric model for the propensity of missing covariate data is assumed. Asymptotic properties of the proposed estimator and the empirical likelihood ratio test statistic are derived. Some simulation results and a real data analysis are presented for illustration.