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High Dimensional Factor Analysis with Weak Factors
This paper studies the principal components (PC) estimator for high dimensional approximate factor models with weak factors in that the factor loading ($\boldsymbol{\Lambda}^0$) scales sublinearly in the number $N$ of cross-section units, i. e., $\boldsymbol{\Lambda}^{0\top} \boldsymbol{\Lambda}^0 / N^\alpha$ is positive definite in the limit for some $\alpha \in (0, 1)$.
Inference for Low-rank Models without Estimating the Rank
This paper studies the inference about linear functionals of high-dimensional low-rank matrices.
Inference for Low-rank Completion without Sample Splitting with Application to Treatment Effect Estimation
This paper studies the inferential theory for estimating low-rank matrices.
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