Citation:
Nilanjana Laha, Nathan Huey, Brent Coull, and Rajarshi Mukherjee. 11/17/2023. “On Statistical Inference with High-Dimensional Sparse CCA.” Inf inference, 12, 4. Publisher's Version
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.
Notes:
Link to our R package, which implements our method.