MECfda: An R Package for Bias Correction Due to Measurement Error in Functional and Scalar Covariates in Scalar-on-Function Regression Models
Methodology
2025-10-29 v2 Computation
Abstract
Functional data analysis (FDA) deals with high-resolution data recorded over a continuum, such as time, space or frequency. Device-based assessments of physical activity or sleep are objective yet still prone to measurement error. We present MECfda, an R package that (i) fits scalar-on-function, generalized scalar-on-function, and functional quantile regression models, and (ii) provides bias-corrected estimation when functional covariates are measured with error. By unifying these tools under a consistent syntax, MECfda enables robust inference for FDA applications that involve noisy functional data.
Keywords
Cite
@article{arxiv.2510.21661,
title = {MECfda: An R Package for Bias Correction Due to Measurement Error in Functional and Scalar Covariates in Scalar-on-Function Regression Models},
author = {Heyang Ji and Ufuk Beyaztas and Nicolas Escobar-Velasquez and Yuanyuan Luan and Xiwei Chen and Mengli Zhang and Roger Zoh and Lan Xue and Carmen Tekwe},
journal= {arXiv preprint arXiv:2510.21661},
year = {2025}
}