arXiv subjects

Roger Zoh

Publications and source records attributed to Roger Zoh.

4 recordsLinked to original sources

arXiv2026

A Bayesian Functional Accelerated Failure-Time Model with Varying Effects Correcting for Measurement Error

Functional data collected as continuously observed trajectories arise naturally in many biomedical settings, and a key inferential goal is understanding how such functional covariates relate to time-to-event outcomes while allowing that relationship to vary across subgroups defined by scalar characteristics. Existing frequentist approaches to functional accelerated failure-time (AFT) models struggle to flexibly capture the joint, nonlinear influence of scalar covariates and time on the functional effect, and none adequately address the measurement error that frequently contaminates functionally observed exposures. We propose a Bayesian functional AFT model in which the functional coefficient is a varying effect function of both time and a set of scalar covariates, modeled through a Gaussian process single-index structure that provides a flexible, nonlinear framework for subgroup modification. Measurement error in the functional covariate is handled by pairing a proxy observation with an instrumental variable that accommodates non-linear associations with the latent functional exposure. A prominent source of such functional data is wearable devices, which can continuously monitor physical activity (PA) behavioral patterns over time, yet whose outputs are well known to be prone to measurement error and to exhibit heterogeneous associations with health outcomes across demographic subgroups. Through simulations, we show that our approach recovers the true varying functional effects and reduces bias relative tona\"ive models that ignore measurement error. We apply our methods to the Reasons for Geographical and Racial Differences in Stroke (REGARDS) study to investigate how step-count physical activity relates to time-to-death from ischemic stroke across racial and regional groups.

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arXiv2025

MECfda: An R Package for Bias Correction Due to Measurement Error in Functional and Scalar Covariates in Scalar-on-Function Regression Models

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.

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arXiv2025

Clustering of functional data prone to complex heteroscedastic measurement error

Several factors make clustering of functional data challenging, including the infinite-dimensional space to which observations belong and the lack of a defined probability density function for the functional random variable. To overcome these barriers, researchers either assume that observations belong to a finite-dimensional space spanned by basis functions or apply nonparametric smoothing methods to the functions prior to clustering. Although extensive literature describes clustering methods for functional data, few studies have explored the clustering of measurement error--prone function-valued data. In this work, we consider clustering methods for functional data prone to complex, heteroscedastic measurement errors. Two stage-based methods using mixed-effects models are first applied to adjust for measurement error bias, followed by cluster analysis of the measurement error--adjusted curves. Through simulations, we investigate how varying sample size, the magnitude of measurement error, and the presence of complex heteroscedastic measurement errors influence the cluster analysis of functional data. Our results indicate that failing to account for measurement errors and the correlation structures associated with frequently collected functional data reduces the accuracy of identifying the true latent groups or clusters. The method consistently produces better results regardless of the initial clustering values used. Moreover, it is flexible and can be applied to various clustering approaches, based on the specific distribution of the data. The developed methods are applied to two data sets: a school-based study of energy expenditure among elementary school-aged children in Texas and data from the National Health and Nutrition Examination Survey on participants' physical activity monitored by wearable devices at frequent intervals.

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arXiv2024

Clustering functional data with measurement errors: a simulation-based approach

Clustering analysis of functional data, which comprises observations that evolve continuously over time or space, has gained increasing attention across various scientific disciplines. Practical applications often involve functional data that are contaminated with measurement errors arising from imprecise instruments, sampling errors, or other sources. These errors can significantly distort the inherent data structure, resulting in erroneous clustering outcomes. In this paper, we propose a simulation-based approach designed to mitigate the impact of measurement errors. Our proposed method estimates the distribution of functional measurement errors through repeated measurements. Subsequently, the clustering algorithm is applied to simulated data generated from the conditional distribution of the unobserved true functional data given the observed contaminated functional data, accounting for the adjustments made to rectify measurement errors. We illustrate through simulations show that the proposed method has improved numerical performance than the naive methods that neglect such errors. Our proposed method was applied to a childhood obesity study, giving more reliable clustering results

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