On estimation and prediction in a spatial semi-functional linear regression model

TL;DR

This paper develops estimation and prediction methods for a spatial semi-functional linear regression model combining parametric and nonparametric components, with proven convergence rates and practical applications.

Contribution

It introduces a novel estimation approach using moments and local linear methods for a semi-functional model with spatial data, and establishes its convergence properties.

Findings

Convergence rates for estimators and predictors are established.

Simulation studies validate the theoretical results.

Application to ozone pollution prediction demonstrates practical utility.

Abstract

We tackle estimation and prediction at non-visted sites in a spatial semi-functional linear regression model with derivatives that combines a functional linear model with a nonparametric regression one. The parametric part is estimated by a method of moments and the other one by a local linear estimator. We establish the convergence rate of the resulting estimators and predictor. A simulation study and an application to ozone pollution prediction at non-visted sites are proposed to illustrate our results.