Name
Abstract | ||
|---|---|---|
Corrupted data sets containing noisy or missing observations are prevalent in various contemporary applications such as economics, finance and bioinformatics. Despite the recent methodological and algorithmic advances in high-dimensional multi-response regression, how to achieve scalable and interpretable estimation under contaminated covariates is unclear. In this paper, we develop a new methodology called convex conditioned sequential sparse learning (COSS) for error-in-variables multi-response regression under both additive measurement errors and random missing data. It combines the strengths of the recently developed sequential sparse factor regression and the nearest positive semi-definite matrix projection, thus enjoying stepwise convexity and scalability in large-scale association analyses. Comprehensive theoretical guarantees are provided and we demonstrate the effectiveness of the proposed methodology through numerical studies. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.jmva.2020.104644 | Journal of Multivariate Analysis |
Keywords | DocType | Volume |
62H12,62H25,62J07 | Journal | 179 |
ISSN | Citations | PageRank |
0047-259X | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wu J. | 1 | 0 | 0.34 |
| Zhiming Zheng | 2 | 128 | 16.80 |
| Yue Li | 3 | 6 | 10.29 |
| Zhang Y. | 4 | 0 | 0.34 |