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Variable Selection for Global Frechet Regression
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DOI：10.1080/01621459.2021.1969240.png)
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Abstract
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Global Frechet regression is an extension of linear regression to cover more general types of responses, such as distributions, networks, and manifolds, which are becoming more prevalent. In such models, predictors are Euclidean while responses are metric space valued. Predictor selection is of major relevance for regression modeling in the presence of multiple predictors but has not yet been addressed for Frechet regression. Due to the metric space-valued nature of the responses, Frechet regression models do not feature model parameters, and this lack of parameters makes it a major challenge to extend existing variable selection methods for linear regression to global Frechet regression. In this work, we address this challenge and propose a novel variable selection method that overcomes it and has good practical performance. We provide theoretical support and demonstrate that the proposed variable selection method achieves selection consistency. We also explore the finite sample performance of the proposed method with numerical examples and data illustrations.
Keywords:
Distributions
Euclidean predictors
Important predictors
Metric space-valued data
Ridge regression
Spherical data
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