This paper tackles the problem of constructing a non-parametric predictor when the latent variables are given with incomplete information. The convenient predictor for this task is the random forest algorithm in conjunction to the so-called CART criterion. The proposed technique enables a partial imputation of the missing values in the data set in a way that suits both a consistent estimator of the regression function as well as a partial recovery of the missing values. A proof of the consistency of the random forest estimator is given in the case where each latent variable is missing completely at random (MCAR).

翻译：本文处理在潜在变量得到不完整信息时构建非参数预测器的问题。 此项任务的方便预测器是随机森林算法,与所谓的 CART 标准相结合。 拟议的技术可以对数据集中缺失的值进行部分估算, 既适合对回归函数的一致估计,也适合对缺失值的部分恢复。 当每个潜在变量完全随机缺失时, 提供了随机森林估计器一致性的证据( MCAR ) 。