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| Main Authors: | , , |
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| Format: | Artículo Open Access |
| Published: |
Wiley
2025
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| Subjects: | |
| Online Access: | https://onlinelibrary.wiley.com/doi/10.1002/cjs.70002 |
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Table of Contents:
- Noisy matrix completion for longitudinal data with subject‐ and time‐specific covariates Zhaohan Sun Yeying Zhu Joel A. Dubin Canadian Journal of Statistics AbstractIn this article, we consider the imputation of missing responses in a longitudinal dataset via matrix completion. We propose a fixed‐effect, longitudinal, low‐rank model that incorporates both subject‐specific and time‐specific covariates. To solve the optimization problem, a two‐step optimization algorithm is proposed, which provides good statistical properties for the estimation of the fixed effects and the low‐rank term. In a theoretical investigation, the non‐asymptotic error bounds on the fixed effects and low‐rank term are presented. We illustrate the finite‐sample performance of the proposed algorithm via simulation studies, and apply our method to a power plant SO emissions dataset in which the monthly recorded amounts of emissions data on monitors are subject to missingness. 10.1002/cjs.70002 http://creativecommons.org/licenses/by/4.0/