Conditional independence testing in Hilbert spaces with applications to functional data analysis
Anton Rask Lundborg, Rajen D. Shah, Jonas Peters
Journal of the Royal Statistical Society: Series B (Statistical Methodology) · 2022 · Volume 84 · Issue 5
Abstract
We study the problem of testing the null hypothesis thatXandYare conditionally independent givenZ, where each ofX,YandZmay be functional random variables. This generalises testing the significance ofXin a regression model of scalar responseYon functional regressorsXandZ. We show, however, that even in the idealised setting where additionally (X,Y,Z) has a Gaussian distribution, the power of any test cannot exceed its size. Further modelling assumptions are needed and we argue that a convenient way of specifying these assumptions is based on choosing methods for regressing each ofXandYonZ. We propose a test statistic involving inner products of the resulting residuals that is simple to compute and calibrate: type I error is controlled uniformly when the in-sample prediction errors are sufficiently small. We show this requirement is met by ridge regression in functional linear model settings without requiring any eigen-spacing conditions or lower bounds on the eigenvalues of the covariance of the functional regressor. We apply our test in constructing confidence intervals for truncation points in truncated functional linear models and testing for edges in a functional graphical model for EEG data.
Keywords
functional graphical model, function-on-function regression, significance testing, truncated functional linear model, uniform type I error control
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