Abstract
In this paper, we consider the statistical inference for several low-rank tensor models. Specifically, in the Tucker low-rank tensor PCA or regression model, provided with any estimates achieving some attainable error rate, we develop the data-driven confidence regions for the singular subspace of the parameter tensor based on the asymptotic distribution of an updated estimate by two-iteration alternating minimization. The asymptotic distributions are established under some essential conditions on the signal-to-noise ratio (in PCA model) or sample size (in regression model). If the parameter tensor is further orthogonally decomposable, we develop the methods and nonasymptotic theory for inference on each individual singular vector. For the rank-one tensor PCA model, we establish the asymptotic distribution for general linear forms of principal components and confidence interval for each entry of the parameter tensor. Finally, numerical simulations are presented to corroborate our theoretical discoveries.
In all of these models, we observe that different from many matrix/vector settings in existing work, debiasing is not required to establish the asymptotic distribution of estimates or to make statistical inference on low-rank tensors. In fact, due to the widely observed statistical-computational-gap for low-rank tensor estimation, one usually requires stronger conditions than the statistical (or information-theoretic) limit to ensure the computationally feasible estimation is achievable. Surprisingly, such conditions “incidentally” render a feasible low-rank tensor inference without debiasing.
Funding Statement
Dong Xia’s research was partially supported by Hong Kong RGC Grant ECS 26302019 and GRF 16303320.
Anru R. Zhang and Yuchen Zhou’s research was partially supported by NSF Grants CAREER-1944904, NSF DMS-1811868 and grants from Wisconsin Alumni Research Foundation (WARF).
Acknowledgments
The authors thank the Editor, the Associate Editor and three anonymous referees for their comments that helped to improve the presentation of this paper.
The authors are listed alphabetically.
This work was done while Anru R. Zhang and Yuchen Zhou were at the University of Wisconsin-Madison.
Anru R. Zhang is the corresponding author.
Citation
Dong Xia. Anru R. Zhang. Yuchen Zhou. "Inference for low-rank tensors—no need to debias." Ann. Statist. 50 (2) 1220 - 1245, April 2022. https://doi.org/10.1214/21-AOS2146
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