Publication Type

Journal Article

Version

publishedVersion

Publication Date

4-2025

Abstract

This article investigates statistical inference for noisy matrix completion in a semi-supervised model when auxiliary covariates are available. The model consists of two parts. One part is a low-rank matrix induced by unobserved latent factors; the other part models the effects of the observed covariates through a coefficient matrix which is composed of high-dimensional column vectors. We model the observational pattern of the responses through a logistic regression of the covariates, and allow its probability to go to zero as the sample size increases. We apply an iterative least squares (LS) estimation approach in our considered context. The iterative LS methods in general enjoy a low computational cost, but deriving the statistical properties of the resulting estimators is a challenging task. We show that our method only needs a few iterations, and the resulting entry-wise estimators of the low-rank matrix and the coefficient matrix are guaranteed to have asymptotic normal distributions. As a result, individual inference can be conducted for each entry of the unknown matrices. We also propose a simultaneous testing procedure with multiplier bootstrap for the high-dimensional coefficient matrix. This simultaneous inferential tool can help us further investigate the effects of covariates for the prediction of missing entries. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.

Keywords

Auxiliary covariates, Factor models, Missing data, Multiplier bootstrap, Simultaneous inference

Discipline

Econometrics | Statistical Models

Research Areas

Econometrics

Publication

Journal of the American Statistical Association

Volume

120

Issue

549

First Page

343

Last Page

355

ISSN

0162-1459

Identifier

10.1080/01621459.2024.2335591

Publisher

Taylor and Francis Group

Copyright Owner and License

Publisher-CC-NC-ND

Additional URL

https://doi.org/10.1080/01621459.2024.2335591

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