Functional Measurement Error in Functional Regression

TL;DR

This paper introduces a novel method for estimating the slope function in generalized functional linear models that accounts for measurement errors in functional data, extending existing techniques to infinite-dimensional spaces.

Contribution

It develops a new framework extending the conditional-score method to handle functional measurement errors in generalized functional linear models with scalar responses.

Findings

The proposed estimator outperforms naive methods ignoring measurement error.

Asymptotic properties of the estimator are established.

Simulation and real data analysis demonstrate improved accuracy.

Abstract

Measurement error is an important problem that has not been very well studied in the context of Functional Data Analysis. To the best of our knowledge, there are no existing methods that address the presence of functional measurement errors in generalized functional linear models. A framework is proposed for estimating the slope function in the presence of measurement error in the generalized functional linear model with a scalar response. This work extends the conditional-score method to the case when both the measurement error and the independent variables lie in an infinite dimensional space. Asymptotic results are obtained for the proposed estimate and its behavior is studied via simulations, when the response is continuous or binary. It's performance on real data is demonstrated through a simulation study based on the Canadian Weather data-set, where errors are introduced in the data-set and it is observed that the proposed estimate indeed performs better than a naive estimate that ignores the measurement error.