Truncated estimation for varying-coefficient functional linear model

TL;DR

This paper introduces a truncated estimation method for varying-coefficient functional linear models, enabling identification of relevant time points in the predictor-response relationship influenced by an exogenous variable.

Contribution

It proposes a penalized likelihood approach with sparsity penalty to determine relevant time points in the functional predictor for varying-coefficient models.

Findings

Effective in simulation studies

Identifies relevant time points in crop yield data

Demonstrates practical applicability

Abstract

Varying-coefficient functional linear models consider the relationship between a response and a predictor, where the response depends not only the predictor but also an exogenous variable. It then accounts for the relation of the predictors and the response varying with the exogenous variable. We consider the method for truncated estimation for varying-coefficient models. The proposed method can clarify to what time point the functional predictor relates to the response at any value of the exogenous variable by investigating the coefficient function. To obtain the truncated model, we apply the penalized likelihood method with the sparsity inducing penalty. Simulation studies are conducted to investigate the effectiveness of the proposed method. We also report the application of the proposed method to the analysis of crop yield data to investigate when an environmental factor relates to the crop yield at any season.