Abstract Missing data occur in almost every field and a great deal of literature has been established for the analysis of missing data with different types of missing mechanisms and under various models. Nonignorable missing data can be analyzed using nonparametric transformation models, which has not been discussed in the literature. In particular, assume that the conditional response probability can be written as the product of separate unknown functions of the response variable and covariates, respectively. For estimation of regression parameters, a weighted rank (WR) estimation procedure is proposed and the asymptotic properties of the resulting WR estimator are established. For the determination of the proposed estimator, a simple coordinate-wise optimization algorithm is developed, and a numerical study is conducted for assessing the performance of the proposed approach and suggests that it works well in practice. An illustration is also provided.

中文翻译：

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具有不可忽略缺失数据的非参数变换模型的加权秩估计

摘要 缺失数据几乎存在于各个领域，并且已经建立了大量文献来分析不同类型缺失机制和各种模型下的缺失数据。可以使用非参数转换模型分析不可忽略的缺失数据，这在文献中尚未讨论。特别地，假设条件响应概率可以分别写为响应变量和协变量的单独未知函数的乘积。对于回归参数的估计，提出了加权秩 (WR) 估计程序，并建立了所得 WR 估计量的渐近特性。为了确定建议的估计量，开发了一种简单的坐标优化算法，并且进行了数值研究以评估所提出方法的性能，并表明它在实践中运行良好。还提供了插图。