De-Biased Sparse PCA: Inference for Eigenstructure of Large Covariance Matrices

Sparse principal component analysis has become one of the most widely used techniques for dimensionality reduction in high-dimensional datasets. While many methods are available for point estimation of eigenstructure in high-dimensional settings, in this paper we propose methodology for uncertainty quantification, such as construction of confidence intervals and tests for the principal eigenvector and the corresponding largest eigenvalue. We base our methodology on an M-estimator with Lasso penalty which achieves minimax optimal rates and is used to construct a de-biased sparse PCA estimator. The novel estimator has a Gaussian limiting distribution and can be used for hypothesis testing or support recovery of the first eigenvector. The empirical performance of the new estimator is demonstrated on synthetic data and we also show that the estimator compares favourably with the classical PCA in moderately high-dimensional regimes.

Automated summary

Sparse principal component analysis has become widely used for dimensionality reduction, and this paper proposes a methodology for uncertainty quantification with construction of confidence intervals and tests for the principal eigenvector. The novel estimator achieves minimax optimal rates, has a Gaussian limiting distribution, and can be used for hypothesis testing and support recovery of the first eigenvector. The empirical performance of the new estimator is demonstrated on synthetic data and shown to compare favorably with classical PCA in moderately high-dimensional regimes.