Conditional Independence Testing in Hilbert Spaces with Applications to Functional Data Analysis

Abstract

We study the problem of testing the null hypothesis that X and Y are conditionally independent given Z, where each of X, Y and Z may be functional random variables. This generalises testing the significance of X in a regression model of scalar response Y on functional regressors X and Z. 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 is based on choosing methods for regressing each of X and Y on Z. 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.

arXiv commentTo appear in JRSSB

References in corpus (11)

• Functional linear regression analysis for longitudinal data
• Prediction in functional linear regression
• Methodology and convergence rates for functional linear regression
• A reproducing kernel Hilbert space approach to functional linear regression
• Methodology and theory for partial least squares applied to functional data
• The Hardness of Conditional Independence Testing and the Generalised Covariance Measure
• Functional additive regression
• Statistical mechanics of neocortical interactions: Canonical momenta indicators of electroencephalography
• Partial Separability and Functional Graphical Models for Multivariate Gaussian Processes
• Bayesian Graphical Models for Multivariate Functional Data
• Uniform approximation in classical weak convergence theory

Cited by in corpus (4)

• Higher Order Kernel Mean Embeddings to Capture Filtrations of Stochastic Processes
• Nonparametric Conditional Local Independence Testing
• High-dimensional Functional Graphical Model Structure Learning via Neighborhood Selection Approach
• Algorithm-agnostic significance testing in supervised learning with multimodal data