Functional Data Analysis

Latest papers

12 papers2 papers in the last four weeks, down 33% on the four weeks before. 0.0% of all new papers.

Joint Modeling of Conditional Mean and Variability in Multilevel Functional Data: Application to Postprandial Glucose Trajectories in the AEGIS Study
Nov 5, 2026Yang Xiao, Francisco Gude, Marcos MatabuenaMixed-Effects Models Functional Data Analysis
Division of Biological and Life Sciences, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates · Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Santiago de Compostela, Spain · Department of Medicine, University of Santiago de Compostela, Santiago de Compostela, Spain
Synthetic Data Augmentation for Functional Data: Wasserstein Risk Bounds and Diffusion-Based Conditional Generation
Oct 25, 2026Yuhao Qi, Wenlin Dai, Xuehu ZhuSynthetic Data Augmentation Data Augmentation
Institute of Statistics and Big Data, Renmin University of China, Beijing, China · School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, China
Functional Autoencoders for Amplitude-Phase Representation Learning
Sep 28, 2026Peida Wu, Xinyang Xiong, Pengcheng ZengFunctional Data Analysis Representation Learning
School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA, USA · Institute of Mathematical Sciences, ShanghaiTech University, Shanghai, China
Geometric Feature Learning for Functional Data Valued on the Symmetric Positive Definite Manifold
Sep 24, 2026Samuel V. Singh, Mimi ZhangGeometric Representation Learning Functional Data Analysis
School of Computer Science and Statistics Trinity College Dublin Dublin, Ireland
Beyond Feature Reliability: Repeat-Informed Multifractal Curve Regression for Brain-Age Prediction
Sep 24, 2026Yu Chang, Anzhe Cheng, Jiahao Chen +8Functional Data Analysis Brain Age Prediction
University of Southern California, USA · University of Toronto, Canada
A Functional SVD Framework for Regularized Multivariate Functional PCA with Dual Penalization
Sep 13, 2026Yue Zhao, Hossein Haghbin, Rebecca Sanders +1Functional Data Analysis Principal Component Analysis
Division of Biostatistics and Health Data Science, University of Minnesota, USA · Faculty of Intelligent Systems Engineering and Data Science, Persian Gulf University, Iran · Department of Mathematical and Statistical Sciences, Marquette University, USA
Biarchetype analysis for univariate functional data. An application to macroeconomic financial time series
Jun 14, 2026Aleix Alcacer, Rafael Benitez, Vicente J. Bolos +1Functional Data Analysis Financial Time Series
Jaume I University, Castelló 12071, Spain. · University of València, València 46022, Spain.
Sparse Functional Singular Value Decomposition for Biclustering and Triclustering Longitudinal Data
Jun 3, 2026Yue Zhao, Thierry Chekouo, Sandra SafoFunctional Data Analysis Clustering
Division of Biostatistics and Health Data Science University of Minnesota 2221 University Avenue SE2026 Minneapolis, MN 55414, United States
Gaussian mixture models in Hilbert spaces via kernel methods
May 7, 2026Daniel López-Montero, Antonio Álvarez-López, Marcos MatabuenaKernel Mean Embeddings Functional Data Analysis
Friedrich-Alexander-Universität Erlangen-Nürnberg · Universidad Autónoma de Madrid · Mohamed bin Zayed University of Artificial Intelligence
Sparse Bayesian Deep Functional Learning with Structured Region Selection
Feb 24, 2026Xiaoxian Zhu, Yingmeng Li, Shuangge Ma +1Functional Data Analysis Bayesian Neural Networks
School of Statistics and Data Science, Shanghai University of Finance and Economics · Department of Biostatistics, Yale School of Public Health
Smooth Flow Matching for Synthesizing Functional Data
Aug 19, 2025Jianbin Tan, Anru R. ZhangFunctional Data Analysis Flow Matching
Department of Biostatistics & Bioinformatics, Duke University, Durham, NC, USA · Department of Computer Science, Duke University, Durham, NC, USA
funOCLUST: Clustering Functional Data with Outliers
Jul 31, 2025Katharine M. Clark, Paul D. McNicholasFunctional Data Analysis Clustering
Department of Mathematics & Statistics, Trent University, Ontario, Canada. · Department of Mathematics & Statistics, McMaster University, Ontario, Canada.

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Joint Modeling of Conditional Mean and Variability in Multilevel Functional Data: Application to Postprandial Glucose Trajectories in the AEGIS Study

Yang Xiao, Francisco Gude, Marcos Matabuena

Division of Biological and Life Sciences, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, United Arab Emirates · Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS), Santiago de Compostela, Spain · Department of Medicine, University of Santiago de Compostela, Santiago de Compostela, Spain

Mixed-Effects Models Functional Data Analysis Heteroscedastic Regression Distributional Regression

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Functional data analysis provides a framework for studying time-varying associations between covariates and high-frequency biological signals. In digital health, participants often contribute repeated functional trajectories, yet existing multilevel regression methods largely focus on the conditional mean, overlooking variability and its role in biological heterogeneity. We propose a heteroskedastic functional mixed-effects regression framework that jointly models the conditional mean and variance of repeated trajectories, with participant-specific random effects in both components. We also develop a participant-level bootstrap to quantify uncertainty in estimated functional parameters. We apply the method to postprandial glucose trajectories from the AEGIS study. Premeal glucose, carbohydrate intake, and meal type show distinct time-dependent associations with glucose variability, whereas HbA1c is more strongly associated with the conditional mean than with variability. Substantial between-participant differences in residual variability remain even after accounting for observed covariates. These findings show that modeling conditional variability alongside the mean provides insights into glucose regulation and individual heterogeneity that mean-focused analyses may miss. More broadly, the proposed framework enables joint characterization of average responses, variability, and participant-specific heterogeneity in repeated biological signals.