Computational Efficient Informative Nonignorable Matrix Completion: A Row- and Column-Wise Matrix U-Statistic Pseudo-Likelihood Approach

• URL: http://arxiv.org/abs/2504.04016v1
• Date: Sat, 05 Apr 2025 01:41:53 GMT
• Title: Computational Efficient Informative Nonignorable Matrix Completion: A Row- and Column-Wise Matrix U-Statistic Pseudo-Likelihood Approach
• Authors: Yuanhong A, Guoyu Zhang, Yongcheng Zeng, Bo Zhang,
• Abstract summary: We establish a unified framework to deal with the high dimensional matrix completion problem.<n>We derive a row- and column-wise matrix U-statistics type loss function, with the nuclear norm for regularization.<n>A singular value proximal gradient algorithm is developed to solve the proposed optimization problem.
• Score: 2.2306682526405868
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this study, we establish a unified framework to deal with the high dimensional matrix completion problem under flexible nonignorable missing mechanisms. Although the matrix completion problem has attracted much attention over the years, there are very sparse works that consider the nonignorable missing mechanism. To address this problem, we derive a row- and column-wise matrix U-statistics type loss function, with the nuclear norm for regularization. A singular value proximal gradient algorithm is developed to solve the proposed optimization problem. We prove the non-asymptotic upper bound of the estimation error's Frobenius norm and show the performance of our method through numerical simulations and real data analysis.

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