Locally sparse quantile estimation for a partially functional interaction model.

ABSTRACT: Functional data analysis has been extensively conducted. In this study, we consider a partially functional model, under which some covariates are scalars and have linear effects, while some other variables are functional and have unspecified nonlinear effects. Significantly advancing from the existing literature, we consider a model with interactions between the functional and scalar covariates. To accommodate long-tailed error distributions which are not uncommon in data analysis, we adopt the quantile technique for estimation. To achieve more interpretable estimation, and to accommodate many practical settings, we assume that the functional covariate effects are locally sparse (that is, there exist subregions on which the effects are exactly zero), which naturally leads to a variable/mod

SUBMITTER: Liang W 

PROVIDER: S-EPMC11566403 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature