Corpus ID: 236428152

Inference for Heteroskedastic PCA with Missing Data

@article{Yan2021InferenceFH,
  title={Inference for Heteroskedastic PCA with Missing Data},
  author={Yuling Yan and Yuxin Chen and Jianqing Fan},
  journal={ArXiv},
  year={2021},
  volume={abs/2107.12365}
}
  • Yuling Yan, Yuxin Chen, Jianqing Fan
  • Published 2021
  • Computer Science, Mathematics
  • ArXiv
This paper studies how to construct confidence regions for principal component analysis (PCA) in high dimension, a problem that has been vastly under-explored. While computing measures of uncertainty for nonlinear/nonconvex estimators is in general difficult in high dimension, the challenge is further compounded by the prevalent presence of missing data and heteroskedastic noise. We propose a suite of solutions to perform valid inference on the principal subspace based on two estimators: a… Expand

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