On Statistical Inference with High Dimensional Sparse CCA

TL;DR

This paper develops a bias-corrected inference method for the leading canonical correlation directions in high-dimensional sparse settings, providing theoretical guarantees and extensive numerical validation.

Contribution

It introduces a novel loss function enabling one-step bias correction for high-dimensional sparse CCA, with adaptive theoretical guarantees.

Findings

Effective bias correction in high-dimensional sparse CCA.

Theoretical adaptivity over covariance structures.

Validated through extensive numerical experiments.

Abstract

We consider asymptotically exact inference on the leading canonical correlation directions and strengths between two high dimensional vectors under sparsity restrictions. In this regard, our main contribution is the development of a loss function, based on which, one can operationalize a one-step bias-correction on reasonable initial estimators. Our analytic results in this regard are adaptive over suitable structural restrictions of the high dimensional nuisance parameters, which, in this set-up, correspond to the covariance matrices of the variables of interest. We further supplement the theoretical guarantees behind our procedures with extensive numerical studies.