Controlling the false discovery rate for latent factors via unit-rank deflation. (English) Zbl 1473.62203
Summary: While sparse factor regression is often encountered under high dimensions, it still is unclear how to control the false discovery rate (FDR) of latent factors. In this paper, we propose a variable selection procedure to address the issue and prove that the FDR can be asymptotically controlled at a target level. Moreover, our approach is scalable and memory-efficient in practice owing to the divide-and-conquer strategy.
MSC:
| 62H25 | Factor analysis and principal components; correspondence analysis |
| 62J07 | Ridge regression; shrinkage estimators (Lasso) |
Software:
SOFARReferences:
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