Functional single index models for longitudinal data

TL;DR

This paper introduces a novel single-index model tailored for longitudinal and functional data, capturing time-dynamic effects and providing efficient estimation methods with proven statistical properties.

Contribution

It proposes a new single-index model for longitudinal data, with consistent and asymptotically normal estimators, and addresses nonparametric regression estimation with optimal convergence rates.

Findings

Estimator is $\

with $\

finite-sample performance is demonstrated numerically.

Abstract

A new single-index model that reflects the time-dynamic effects of the single index is proposed for longitudinal and functional response data, possibly measured with errors, for both longitudinal and time-invariant covariates. With appropriate initial estimates of the parametric index, the proposed estimator is shown to be $\sqrt{n}$-consistent and asymptotically normally distributed. We also address the nonparametric estimation of regression functions and provide estimates with optimal convergence rates. One advantage of the new approach is that the same bandwidth is used to estimate both the nonparametric mean function and the parameter in the index. The finite-sample performance for the proposed procedure is studied numerically.