High-Dimensional Varying Coefficient Models with Functional Random Effects
arXiv:2110.06426
Abstract
We consider a sparse high-dimensional varying coefficients model with random effects, a flexible linear model allowing covariates and coefficients to have a functional dependence with time. For each individual, we observe discretely sampled responses and covariates as a function of time as well as time invariant covariates. Under sampling times that are either fixed and common or random and independent amongst individuals, we propose a projection procedure for the empirical estimation of all varying coefficients. We extend this estimator to construct confidence bands for a fixed number of varying coefficients.
References in corpus (5)
- Simultaneous analysis of Lasso and Dantzig selector
- Optimal estimation of the mean function based on discretely sampled functional data: Phase transition
- Bayesian Estimation of Population-Level Trends in Measures of Health Status
- Scalable high-dimensional Bayesian varying coefficient models with unknown within-subject covariance
- Inference of high-dimensional linear models with time-varying coefficients