Empirical likelihood inference for partial functional linear regression models based on B spline

TL;DR

This paper develops an empirical likelihood approach for inference in partial functional linear regression models using B spline basis, demonstrating theoretical convergence and finite sample performance through simulations.

Contribution

It introduces a novel empirical likelihood inference method for partial functional linear models based on B spline, with proven asymptotic properties.

Findings

Empirical log likelihood ratio converges to a weighted sum of chi-square distributions.

Simulation results show good finite sample performance.

Method provides a new inference tool for functional linear models.

Abstract

In this paper, we apply empirical likelihood method to inference for the regression parameters in the partial functional linear regression models based on B spline. We prove that the empirical log likelihood ratio for the regression parameters converges in law to a weighted sum of independent chi square distributions and run simulations to assess the finite sample performance of our method.