45 citations · 56 across the 16 of their papers we have counts for
18 papers
Deep Learning for Random Objects in Metric Spaces: Beyond Manifold Assumptions
Marcos Matabuena, Xiaokai Luo, Oscar Hernan Madrid Padilla
Deep learning (DL) methods have substantially improved predictive performance across a wide range of downstream prediction tasks, enabling new discoveries in multiple domains. Howe…
ROC Analysis with Covariate Adjustment Using Neural Network Models: Evaluating the Role of Age in the Physical Activity-Mortality Association
Ziad Akram Ali Hammouri, Yating Zou, Rahul Ghosal +2
The receiver operating characteristic (ROC) curve and its summary measure, the Area Under the Curve (AUC), are well-established tools for evaluating the efficacy of biomarkers in b…
Model-Free Kernel Conformal Depth Measures Algorithm for Uncertainty Quantification in Regression Models in Separable Hilbert Spaces
Marcos Matabuena, Rahul Ghosal, Pavlo Mozharovskyi +2
Depth measures are powerful tools for defining level sets in emerging, non--standard, and complex random objects such as high-dimensional multivariate data, functional data, and ra…
Variable Selection for Fixed and Random Effects in Multilevel Functional Mixed Effects Models
Rahul Ghosal, Marcos Matabuena, Enakshi Saha
We develop a new method for simultaneously selecting fixed and random effects in a multilevel functional regression model. The proposed method is motivated by accelerometer-derived…
Glucodensity Functional Profiles Outperform Traditional Continuous Glucose Monitoring Metrics
Marcos Matabuena, Rahul Ghosal, Javier Enrique Aguilar +6
Continuous glucose monitoring (CGM) data has revolutionized the management of type 1 diabetes, particularly when integrated with insulin pumps to mitigate clinical events such as h…
Functional Time Transformation Model with Applications to Digital Health
Rahul Ghosal, Marcos Matabuena, Sujit K. Ghosh
The advent of wearable and sensor technologies now leads to functional predictors which are intrinsically infinite dimensional. While the existing approaches for functional data an…
Conformal uncertainty quantification using kernel depth measures in separable Hilbert spaces
Marcos Matabuena, Rahul Ghosal, Pavlo Mozharovskyi +2
Depth measures have gained popularity in the statistical literature for defining level sets in complex data structures like multivariate data, functional data, and graphs. Despite…
Beyond Scalar Metrics: Functional Data Analysis of Postprandial Continuous Glucose Monitoring in the AEGIS Study
Marcos Matabuena, Joe Sartini, Francisco Gude
Postprandial glucose collected through continuous glucose monitoring (CGM) provides critical information for assessing metabolic capacity and guiding dietary recommendations. Tradi…
Optimal Cut-Point Estimation for Functional Digital Biomarkers: Application to Diabetes Risk Stratification via Continuous Glucose Monitoring
Oscar Lado-Baleato, Carla Díaz-Louza, Francisco Gude +1
Establishing optimal cut-offs for clinical biomarkers is a fundamental statistical problem in epidemiology, clinical trials, and drug discovery. While there is extensive literature…
Personalized Imputation in metric spaces via conformal prediction: Applications in Predicting Diabetes Development with Continuous Glucose Monitoring Information
Marcos Matabuena, Carla Díaz-Louzao, Rahul Ghosal +1
The challenge of handling missing data is widespread in modern data analysis, particularly during the preprocessing phase and in various inferential modeling tasks. Although numero…
Screening for Diabetes Mellitus in the U.S. Population Using Neural Network Models and Complex Survey Designs
Marcos Matabuena, Juan C. Vidal, Rahul Ghosal +1
Complex survey designs are commonly employed in many medical cohorts. In such scenarios, developing case-specific predictive risk score models that reflect the unique characteristi…
Conditional Mean and Variance Estimation via \textit{k}-NN Algorithm with Automated Variance Selection
Marcos Matabuena, Juan C. Vidal, Oscar Hernan Madrid Padilla +1
We introduce a novel \textit{k}-nearest neighbor (\textit{k}-NN) regression method for joint estimation of the conditional mean and variance. The proposed algorithm preserves the c…
Multivariate Scalar on Multidimensional Distribution Regression
Rahul Ghosal, Marcos Matabuena
We develop a new method for multivariate scalar on multidimensional distribution regression. Traditional approaches typically analyze isolated univariate scalar outcomes or conside…
Functional proportional hazards mixture cure model and its application to modelling the association between cancer mortality and physical activity in NHANES 2003-2006
Rahul Ghosal, Marcos Matabuena, Jiajia Zhang
We develop a functional proportional hazards mixture cure (FPHMC) model with scalar and functional covariates measured at the baseline. The mixture cure model, useful in studying p…
Kernel Biclustering algorithm in Hilbert Spaces
Marcos Matabuena, J. C Vidal, Oscar Hernan Madrid Padilla +1
Biclustering algorithms partition data and covariates simultaneously, providing new insights in several domains, such as analyzing gene expression to discover new biological functi…
Hypothesis testing for matched pairs with missing data by maximum mean discrepancy: An application to continuous glucose monitoring
Marcos Matabuena, Paulo Félix, Marc Ditzhaus +2
A frequent problem in statistical science is how to properly handle missing data in matched paired observations. There is a large body of literature coping with the univariate case…
Glucose values prediction five years ahead with a new framework of missing responses in reproducing kernel Hilbert spaces, and the use of continuous glucose monitoring technology
Marcos Matabuena, Paulo Félix, Carlos Meijide-Garcia +1
AEGIS study possesses unique information on longitudinal changes in circulating glucose through continuous glucose monitoring technology (CGM). However, as usual in longitudinal me…
Glucodensities: a new representation of glucose profiles using distributional data analysis
Marcos Matabuena, Alexander Petersen, Juan C. Vidal +1
Biosensor data has the potential ability to improve disease control and detection. However, the analysis of these data under free-living conditions is not feasible with current sta…