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Controlling the false discovery rate for latent factors via unit-rank deflation

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  • Dong, Ruipeng
  • Zhou, Jia
  • Zheng, Zemin

Abstract

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.

Suggested Citation

  • Dong, Ruipeng & Zhou, Jia & Zheng, Zemin, 2021. "Controlling the false discovery rate for latent factors via unit-rank deflation," Statistics & Probability Letters, Elsevier, vol. 178(C).
    • Handle: RePEc:eee:stapro:v:178:y:2021:i:c:s0167715221001401
    DOI: 10.1016/j.spl.2021.109178
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    References listed on IDEAS

    as
    1. Yingying Fan & Emre Demirkaya & Gaorong Li & Jinchi Lv, 2020. "RANK: Large-Scale Inference With Graphical Nonlinear Knockoffs," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(529), pages 362-379, January.
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