Inference for Low-rank Completion without Sample Splitting with Application to Treatment Effect Estimation

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Tác giả: Jungjun Choi, Hyukjun Kwon, Yuan Liao

Ngôn ngữ: eng

Ký hiệu phân loại: 511.4 Approximations formerly also 513.24 and expansions

Thông tin xuất bản: 2023

Mô tả vật lý:

Bộ sưu tập: Báo, Tạp chí

ID: 197858

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 probabilities. Empirically, we apply the proposed procedure to estimating the impact of the presidential vote on allocating the U.S. federal budget to the states.
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