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INFERENCE FOR HETEROSKEDASTIC PCA WITH MISSING DATA
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DOI：10.1214/24-AOS2366.png)
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This paper studies how to construct confidence regions for principal component analysis (PCA) in high dimension, a problem that has been vastly underexplored. 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 novel approach to performing valid inference on the principal subspace, on the basis of an estimator called HeteroPCA guarantees for HeteroPCA, and demonstrate how these can be invoked to compute both confidence regions for the principal subspace and entrywise confidence intervals for the spiked covariance matrix. Our inference procedures are fully data-driven and adaptive to heteroskedastic random noise, without requiring prior knowledge about the noise levels.
Keyword:
Principal component analysis
confidence regions
missing data
uncertainty quantification
heteroskedastic data
subspace estimation
期刊
IF:
3.7
论文数:
2.8K
被引数:
2.9W
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