Globally Adaptive Longitudinal Quantile Regression with High Dimensional Compositional Covariates.

ABSTRACT: In this work, we propose a longitudinal quantile regression framework that enables a robust characterization of heterogeneous covariate-response associations in the presence of high-dimensional compositional covariates and repeated measurements of both response and covariates. We develop a globally adaptive penalization procedure, which can consistently identify covariate sparsity patterns across a continuum set of quantile levels. The proposed estimation procedure properly aggregates longitudinal observations over time, and ensures the satisfaction of the sum-zero coefficient constraint that is needed for proper interpretation of the effects of compositional covariates. We establish the oracle rate of uniform convergence and weak convergence of the resulting estimators, and further justif

SUBMITTER: Ma H 

PROVIDER: S-EPMC10361693 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature