B1124
Title: De-biased CCA: Theory and application
Authors: Nilanjana Laha - Texas A\&M University (United States) [presenting]
Rajarshi Mukherjee - Harvard T.H. Chan School of Public Health (United States)
Brent Coull - Harvard University (United States)
Nathatn Huey - Harvard University (United States)
Abstract: Asymptotically exact inference is considered on the leading canonical correlation directions and strengths between two high-dimensional vectors under sparsity restrictions. In this regard, the main contribution is the development of a loss function based on which one can operationalize a one-step bias correction on reasonable initial estimators. The 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. The theoretical guarantees are further supplemented by the procedures with an application in a genomic study.
