Marginal Coordinate Test for Fréchet Regression with Random Objects

• URL: http://arxiv.org/abs/2608.30644v1
• Date: Mon, 31 Aug 2026 11:48:12 GMT
• Title: Marginal Coordinate Test for Fréchet Regression with Random Objects
• Abstract summary: We develop a test for regression with Euclidean predictors and a random-object response in a separable metric space.<n>We establish a weighted centered chi-square null limit, wild bootstrap validity, and consistency against fixed detectable alternatives.<n>For simultaneous inference, truncated p-to-e calibration combined with e-BH provides false discovery rate control under general dependence.
• Score: 15.929485404318557
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We develop a marginal coordinate test for regression with Euclidean predictors and a random-object response in a separable metric space. The goal is to test whether a predictor provides additional information about the response conditional on the remaining predictors. In a semi-supervised design, an unlabeled sample is used to estimate predictor conditional means, while an independent labeled sample is reserved for inference. The resulting residuals are combined with a product-space kernel to form a kernel conditional mean dependence (KCMD) U-statistic without requiring a response residual. The primary identity-based test targets a necessary conditional mean restriction, while a multiple-transformation extension probes broader alternatives. We establish a weighted centered chi-square null limit, wild bootstrap validity, consistency against fixed detectable alternatives, and local power under mean-element alternatives. For simultaneous inference, truncated p-to-e calibration combined with e-BH provides asymptotic false discovery rate control under general dependence. Simulations with Euclidean and non-Euclidean responses, together with a New York City taxi-flow analysis, illustrate the method.

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