Semi-functional partial linear regression under measurement error

S. Novo Díaz, G. Aneiros, P. Vieu

This work explores a semiparametric regression model where the response variable is expressed as the sum of two components. The first is a parametric (linear) term associated with a finite-dimensional (p) explanatory variable, which is subject to additive measurement error. The second component captures the effect of a functional (infinite-dimensional) variable on the response in a nonparametric manner. To estimate each component, we propose k-NN-based estimators and derive some asymptotic properties. A simulation study evaluates their performance in finite sample settings, while an application to real data demonstrates the practical relevance of our approach.

Keywords: errors-in-variables, functional data, semi-functional regression, partially linear models

Nonparametric Estimation
June 12, 2025  7:00 PM
Auditorio 1. Ricard Vinyes

J. Ameijeiras-Alonso, I. Gijbels

M. Alonso Pena, G. Claeskens, I. Gijbels

B. E. Monroy-Castillo, M. A. Jácome, R. Cao

H. González-Vázquez, B. Pateiro-López, A. Rodríguez-Casal