Lee Shen-Ming, Gee Mei-Jih, Hsieh Shu-Hui
Department of Statistics, Feng Chia University, Taiwan.
Biometrics. 2011 Sep;67(3):788-98. doi: 10.1111/j.1541-0420.2010.01499.x. Epub 2010 Oct 29.
We consider the estimation problem of a proportional odds model with missing covariates. Based on the validation and nonvalidation data sets, we propose a joint conditional method that is an extension of Wang et al. (2002, Statistica Sinica 12, 555-574). The proposed method is semiparametric since it requires neither an additional model for the missingness mechanism, nor the specification of the conditional distribution of missing covariates given observed variables. Under the assumption that the observed covariates and the surrogate variable are categorical, we derived the large sample property. The simulation studies show that in various situations, the joint conditional method is more efficient than the conditional estimation method and weighted method. We also use a real data set that came from a survey of cable TV satisfaction to illustrate the approaches.
我们考虑协变量缺失时比例优势模型的估计问题。基于验证数据集和非验证数据集，我们提出了一种联合条件方法，该方法是Wang等人（2002年，《统计学报》12卷，555 - 574页）方法的扩展。所提出的方法是半参数方法，因为它既不需要针对缺失机制的额外模型，也不需要给定观测变量时缺失协变量的条件分布的具体形式。在观测协变量和替代变量为分类变量的假设下，我们推导了大样本性质。模拟研究表明，在各种情况下，联合条件方法比条件估计方法和加权方法更有效。我们还使用了一个来自有线电视满意度调查的真实数据集来说明这些方法。
