Variable Selection for Global Frechet Regression
成果类型：
Article
署名作者：
Tucker, Danielle C.; Wu, Yichao; Mueller, Hans-Georg
署名单位：
University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital; University of California System; University of California Davis
刊物名称：
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
ISSN/ISSBN：
0162-1459
DOI：
10.1080/01621459.2021.1969240
发表日期：
2023
页码：
1023-1037
关键词：
摘要：
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.