Inference for low-rank completion without sample splitting with application to treatment effect estimation
无需样本分割的低秩矩阵补全推断及处理效应应用
This paper studies the inferential theory for estimating low-rank matrices. It also provides an inference method for the average treatment effect as an application. We show that the least square estimation of eigenvectors following the nuclear norm penalization attains the asymptotic normality. The key contribution of our method is that it does not require sample splitting. In addition, this paper allows dependent observation patterns and heterogeneous observation…