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Universidade Mohamed bin Zayed de Intelixencia Artificial Bioestatística dixital Grupo de investigación de Marcos Matabuena

Investigación

O futuro xa está en marcha

Imaxinemos unha persoa que sofre unha síndrome coronaria aguda mentres está soa na casa e leva un dispositivo que monitoriza continuamente as súas constantes vitais. Ao detectar cambios críticos, como un electrocardiograma anómalo ou unha caída do nivel de osíxeno, o dispositivo activa automaticamente unha resposta de emerxencia. En cuestión de segundos, o sistema localiza e mobiliza a ambulancia dispoñible máis próxima, adaptándose en tempo real ao tráfico e á capacidade dos hospitais.

Durante o traslado, o dispositivo transmite continuamente información vital do paciente, o que permite axustar de forma dinámica as decisións clínicas e loxísticas. Se o estado do paciente empeora, avísase o hospital para que prepare unha unidade de coidados críticos. Á súa chegada, os modelos preditivos orientan as decisións para acadar o mellor resultado posible e utilizar os recursos sanitarios coa máxima eficiencia.

A nosa misión e visión

O noso obxectivo a longo prazo é construír un marco de toma de decisións sanitarias que mellore as decisións clínicas e de saúde pública, tanto individuais como poboacionais, combinando bioestatística, tecnoloxías de saúde dixital e investigación operativa.

Animación sobre datos ausentes en teléfonos intelixentes

Actualmente desenvolvemos métodos estatísticos e de intelixencia artificial para problemas clínicos complexos que implican datos de saúde continuos, de alta frecuencia e en tempo real. Queremos que estes métodos respondan ás preguntas médicas actuais e axuden a identificar as máis importantes. Que dieta é a mellor para unha persoa concreta? Canta actividade física precisa para favorecer un envellecemento saudable e a lonxevidade? Como deben cambiar estas recomendacións segundo o seu perfil clínico, a súa bioloxía, o seu contorno e o seu estilo de vida?

Para responder a estas preguntas, a nosa investigación combina métodos modernos de IA estatística para inferencia causal, análise de supervivencia, cuantificación da incerteza e modelización xerativa con efectos aleatorios, co fin de avanzar na aprendizaxe estatística para obxectos aleatorios non euclidianos. O deseño coidadoso dos estudos e a escalabilidade computacional son esenciais. Buscamos métodos matematicamente rigorosos e eficientes a escala poboacional, orientados a recursos biomédicos como UK Biobank, All of Us Research Program, Human Phenotype Project e NHANES.

Human Phenotype Project

Aínda que moitos métodos publicados en revistas metodolóxicas de primeiro nivel son matematicamente rigorosos e tecnicamente sólidos, non sempre se deseñan tendo en conta a complexidade dos datos clínicos reais e os supostos necesarios para analizalos. Por iso, partimos de preguntas biomédicas concretas e desafíos prácticos. Desenvolvemos métodos que reflictan a estrutura dos datos, a realidade da práctica clínica e as decisións críticas de pacientes, profesionais e sistemas sanitarios para facer avanzar o campo.

Sete piares do noso programa de investigación en saúde dixital

Resumo de Glucodensities: A new representation of glucose profiles using distributional data analysis

Glucodensities: A new representation of glucose profiles using distributional data analysis

Marcos Matabuena, Alexander Petersen, Juan C Vidal, Francisco Gude

Os nosos sete artigos representan un avance metodolóxico e conceptual substancial para a saúde dixital.

A introdución da glucodensidade constitúe un primeiro paso cara a fenotipos dixitais máis complexos mediante a análise de datos funcionais e distribucionais. Este marco abre novas vías de descubrimento biolóxico e permite aproveitar con maior eficiencia a información continua dos novos dispositivos médicos, no canto de reducila a unhas poucas medidas escalares.

A glucodensidade multivariante supón un novo avance conceptual na análise da fisioloxía humana. Os datos clínicos deixan de analizarse unicamente como números individuais e pasan a tratarse como obxectos complexos, máis próximos ás imaxes médicas. Agora podemos integrar a dinámica dos procesos fisiolóxicos subxacentes nun marco distribucional unificado de glucodensidade.

O traballo conxunto con Ciprian Crainiceanu sobre modelos de efectos aleatorios para datos de glucodensidade estende estas ideas a estudos lonxitudinais de semanas, meses ou anos. Este traballo crea un novo marco para utilizar información dixital en ensaios clínicos, coa posibilidade de aumentar a potencia estatística e descubrir biomarcadores lonxitudinais novos e máis relevantes.

Xunto con Mateo Cámara, propoñemos o primeiro marco de modelización con efectos aleatorios en espazos métricos xerais, que ofrece un enfoque unificado para analizar distintos tipos de obxectos estatísticos en estudos clínicos. A maior parte da información de saúde dixital é case continua no tempo, mídese en múltiples escalas temporais e presenta unha estrutura multinivel. O noso marco ofrece, por primeira vez, unha vía analítica xeral para modelar estas fontes complexas de variación cunha maior xeneralidade matemática.

O marco de cuantificación da incerteza en espazos métricos, desenvolvido xunto con Gábor Lugosi, é metodoloxicamente importante porque permite calibrar e avaliar formalmente a cobertura condicional sen condicións restritivas de suavidade. Tamén abre un marco máis xeral para cuantificar a incerteza dos biomarcadores do futuro, que se representarán cada vez máis mediante distribucións, funcións, imaxes, redes ou outros obxectos aleatorios non euclidianos complexos.

O traballo cos meus colegas chineses, Mingyang Yi, Zhiming Ma, e Ruoyu Wang, sobre modelos de difusión e erro de medición permite incorporar a incerteza das medicións ás decisións biomédicas. A idea principal é integrar métodos xerais de IA xerativa nos procedementos estatísticos clásicos de erro de medición, para que os erros de sensores, medicións clínicas e outras tecnoloxías biomédicas se incorporen directamente á análise.

Por último, o éxito da nova era dos datos tamén dependerá de novos deseños experimentais e de mostraxe. En colaboración con Yating Zou e Michael Kosorok, propoñemos unha nova metodoloxía de bosques aleatorios para datos de enquisas complexas. O método ten implicacións relevantes para a inferencia, xa que aplica o procedemento bootstrap a nivel de nodo. Isto abre oportunidades teóricas para mellorar a aprendizaxe automática en deseños de mostraxe complexos e, á vez, ofrece unha forma fundamentada de utilizar grandes bases de datos de saúde preservando unha inferencia válida para a poboación obxectivo.

En conxunto, estes traballos definen os piares do noso programa: novas representacións de datos de saúde dixital, modelización lonxitudinal e multinivel, cuantificación da incerteza, corrección do erro de medición mediante IA xerativa e inferencia estatisticamente válida en estudos biomédicos complexos.

Publicacións de anos anteriores

2026

Denoising Data with Measurement Error Using a Reproducing Kernel-based Diffusion Model
Denoising Data with Measurement Error Using a Reproducing Kernel-based Diffusion Model
Mingyang Yi, Marcos Matabuena, Zhiming Ma, Ruoyu Wang
arXiv  ·  14 Jul 2026  ·  arxiv:2501.00212
EpiBinder: a multimodal framework for cell-type-specific prediction and interpretation of transcription factor binding
EpiBinder: a multimodal framework for cell-type-specific prediction and interpretation of transcription factor binding
Ruben Solozabal, Albert Baichorov, Irina Miodownik, Tamir Avioz, Le Song, Marcos Matabuena, Martin Takáč, Ariel Afek
openRxiv  ·  08 Jul 2026  ·  10.64898/2026.07.06.736502
Pre-activity glycemic prediction prioritizes post-meal movement
Pre-activity glycemic prediction prioritizes post-meal movement
Smadar Shilo, Gal Sapir, Guy Lutsker, Yeela Talmor-Barkan, Anastasia Godneva, Alon Diament, Marcos Matabuena, Eran Segal, Hagai Rossman
openRxiv  ·  24 Jun 2026  ·  doi:10.64898/2026.06.22.26356272
Conformal and kNN Predictive Uncertainty Quantification Algorithms in Metric Spaces
Conformal and kNN Predictive Uncertainty Quantification Algorithms in Metric Spaces
Gábor Lugosi, Marcos Matabuena
arXiv  ·  23 Jun 2026  ·  arxiv:2507.15741
A Deep Learning-Based Predictive Algorithm for Metabolic Syndrome Detection in the U.S. Population
A Deep Learning-Based Predictive Algorithm for Metabolic Syndrome Detection in the U.S. Population
Cristina Correa, Ruben Solozabal, Ziad Akram Ali Hammouri, Fernando Gómez-Peralta, Hagai Rossman, Juan C. Vidal, David C. Klonoff, Eran Segal, Marcos Matabuena
openRxiv  ·  02 Jun 2026  ·  doi:10.64898/2026.05.24.26354007
Capturing the Curve: Functional Data Analysis for Validated Digital Outcome Measures
Capturing the Curve: Functional Data Analysis for Validated Digital Outcome Measures
Mia S. Tackney, Marcos Matabuena, Marco Palma, Michael Wester, Claire Maassen, Thomas Krammer, Julian Mustroph, Peter H. Charlton, James Carpenter, Sofia S. Villar
arXiv  ·  28 May 2026  ·  arxiv:2605.28344
Gaussian mixture models in Hilbert spaces via kernel methods
Gaussian mixture models in Hilbert spaces via kernel methods
Daniel López-Montero, Antonio Álvarez-López, Marcos Matabuena
arXiv  ·  08 May 2026  ·  arxiv:2605.05996
Screening for diabetes mellitus in the US population using neural network-based modeling and complex survey designs
Screening for diabetes mellitus in the US population using neural network-based modeling and complex survey designs
Marcos Matabuena, Juan C Vidal, Rahul Ghosal, Jukka-Pekka Onnela
Statistical Methods in Medical Research  ·  07 May 2026  ·  doi:10.1177/09622802261442893
Random-Effects Algorithm for Random Objects in Metric Spaces
Random-Effects Algorithm for Random Objects in Metric Spaces
Marcos Matabuena, Mateo Cámara
arXiv  ·  05 May 2026  ·  arxiv:2605.02693
Continuous-Time Learning of Probability Distributions: A Case Study in a Digital Trial of Young Children with Type 1 Diabetes
Continuous-Time Learning of Probability Distributions: A Case Study in a Digital Trial of Young Children with Type 1 Diabetes
Antonio Álvarez-López, Marcos Matabuena
arXiv  ·  26 Mar 2026  ·  arxiv:2603.24427
Multilevel functional distributional models with applications to continuous glucose monitoring in diabetes clinical trials
Multilevel functional distributional models with applications to continuous glucose monitoring in diabetes clinical trials
Marcos Matabuena, Ciprian M. Crainiceanu
The Annals of Applied Statistics  ·  01 Mar 2026  ·  doi:10.1214/26-AOAS2139
Glucodensity-Based Models Outperform Time in Range and Glycemia Risk Index in Prediction Models
Glucodensity-Based Models Outperform Time in Range and Glycemia Risk Index in Prediction Models
Mandy M. Shao, Agatha F. Scheideman, David C. Klonoff, Francisco Gude, Marcos Matabuena
Journal of Diabetes Science and Technology  ·  23 Feb 2026  ·  doi:10.1177/19322968261421954
Survival on Image Regression With Application to Partially Functional Distributional Representation of Physical Activity
Survival on Image Regression With Application to Partially Functional Distributional Representation of Physical Activity
Rahul Ghosal, Sunwoo Emma Cho, Marcos Matabuena
Statistical Analysis and Data Mining: An ASA Data Science Journal  ·  01 Feb 2026  ·  doi:10.1002/sam.70068
Exploratory analysis of smartphone-based step counts as a digital biomarker for survival in ALS patients
Exploratory analysis of smartphone-based step counts as a digital biomarker for survival in ALS patients
Marcos Matabuena, Marcin Straczkiewicz, Narghes Calcagno, Katherine M. Burke, Timothy B. Royse, Amrita Iyer, Kendall T. Carney, Sydney Hall, James D. Berry, Jukka-Pekka Onnela
Frontiers in Digital Health  ·  29 Jan 2026  ·  doi:10.3389/fdgth.2025.1705368
Beyond scalar metrics: functional data analysis of postprandial continuous glucose monitoring in the AEGIS study
Beyond scalar metrics: functional data analysis of postprandial continuous glucose monitoring in the AEGIS study
Marcos Matabuena, Joseph Sartini, Francisco Gude
BMC Medical Research Methodology  ·  24 Jan 2026  ·  doi:10.1186/s12874-025-02748-2
Distributional Random Forests for Complex Survey Designs on Reproducing Kernel Hilbert Spaces
Distributional Random Forests for Complex Survey Designs on Reproducing Kernel Hilbert Spaces
Yating Zou, Marcos Matabuena, Michael R. Kosorok
arXiv  ·  07 Jan 2026  ·  arxiv:2512.08179
Daily Physical Activity Patterns Enhance Mortality Prediction in NHANES 2011-2014: A Comparative Study Across Classical, Machine Learning, and Deep Learning Models Using Accelerometer Data
Daily Physical Activity Patterns Enhance Mortality Prediction in NHANES 2011-2014: A Comparative Study Across Classical, Machine Learning, and Deep Learning Models Using Accelerometer Data
Srimanta Ghosh, Sunwoo Emma Cho, Yuanzhen Yue, Marcos Matabuena, Enakshi Saha, Rahul Ghosal
Journal for the Measurement of Physical Behaviour  ·  01 Jan 2026  ·  doi:10.1123/jmpb.2025-0052
From Physical Activity to Spirometry as a Curve : A Combined Model for Mortality Prediction
From Physical Activity to Spirometry (as a Curve): A Combined Model for Mortality Prediction
Marcos Matabuena, Marcos Matabuena@mbzuai Ac Ae, Emma Cho, Rahul Ghosal, Cristina Correa, Mateo Cámara, Manuel Oviedo De La Fuente, Holger Cevallos, Francisco Gude
Sen publicar  ·  01 Jan 2026  ·  doi:10.13140/RG.2.2.35806.60486

2025

Continuous Glucose Monitoring as an alternative for Cystic Fibrosis Related diabetes screening and diagnosis
Continuous Glucose Monitoring as an alternative for Cystic Fibrosis Related diabetes screening and diagnosis
I. Aguilera García, P. Vázquez Pérez, B. López Plaza, M. Matabuena, R.M. García Moreno, S.Palma Milla, E. Zamarrón de Lucas, M.C. Prados Sánchez, N. González Pérez de Villar
Diabetes Research and Clinical Practice  ·  01 Nov 2025  ·  doi:10.1016/j.diabres.2025.112952
Unequal strides to longevity. The extra mile to health equity
Unequal strides to longevity. The extra mile to health equity
Borja del Pozo Cruz, Francisco Perales, Marcin Straczkiewicz, Marcos Matabuena, Jesús del Pozo-Cruz, Rubén López-Bueno
Annals of Epidemiology  ·  01 Nov 2025  ·  doi:10.1016/j.annepidem.2025.10.009
kNN Algorithm for Conditional Mean and Variance Estimation with Automated Uncertainty Quantification and Variable Selection
kNN Algorithm for Conditional Mean and Variance Estimation with Automated Uncertainty Quantification and Variable Selection
Marcos Matabuena, Juan C. Vidal, Oscar Hernan Madrid Padilla, Jukka-Pekka Onnela
arXiv  ·  28 Oct 2025  ·  arxiv:2402.01635
Variable Selection for Fixed and Random Effects in Multilevel Functional Mixed Effects Models
Variable Selection for Fixed and Random Effects in Multilevel Functional Mixed Effects Models
Rahul Ghosal, Marcos Matabuena, Enakshi Saha
arXiv  ·  24 Oct 2025  ·  arxiv:2505.05416
ROC Analysis with Covariate Adjustment Using Neural Network Models: Evaluating the Role of Age in the Physical Activity-Mortality Association
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, Juan C. Vidal, Marcos Matabuena
arXiv  ·  20 Oct 2025  ·  arxiv:2510.14494
Glucodensity functional profiles outperform traditional continuous glucose monitoring metrics
Glucodensity functional profiles outperform traditional continuous glucose monitoring metrics
Marcos Matabuena, Rahul Ghosal, Javier Enrique Aguilar, Ayya Keshet, Robert Wagner, Carmen Fernández Merino, Juan Sánchez Castro, Vadim Zipunnikov, Jukka-Pekka Onnela, Francisco Gude
Scientific Reports  ·  29 Sep 2025  ·  doi:10.1038/s41598-025-18119-2
Age, Sex, BMI, Meal Timing, and Glycemic Response to Meal Glycemic Load
Age, Sex, BMI, Meal Timing, and Glycemic Response to Meal Glycemic Load
Mar Calvo-Malvar, Óscar Lado-Baleato, Ana Cao Ríos, Cristina Porca Fernández, Alfonso Benítez-Calvo, Carmen Fernandez-Merino, Juan Sánchez-Castro, Robert Wagner, Marcos Matabuena, Francisco Gude
JAMA Network Open  ·  23 Sep 2025  ·  doi:10.1001/jamanetworkopen.2025.33193
Continuous Temporal Learning of Probability Distributions via Neural ODEs with Applications in Continuous Glucose Monitoring Data
Continuous Temporal Learning of Probability Distributions via Neural ODEs with Applications in Continuous Glucose Monitoring Data
Antonio Álvarez-López, Marcos Matabuena
arXiv  ·  18 Sep 2025  ·  arxiv:2505.08698
Distributional Data Analysis Uncovers Hundreds of Novel and Heritable Phenomic Features from Temporal Cotton and Maize Drone Imagery
Distributional Data Analysis Uncovers Hundreds of Novel and Heritable Phenomic Features from Temporal Cotton and Maize Drone Imagery
Aaron J. DeSalvio, Marcos Matabuena, Alper Adak, Mustafa A. Arik, Serina M. DeSalvio, …, Shawn M. Kaeppler, Dayane C. Lima, Candice N. Hirsch, Addie Thompson, David M. Stelly
openRxiv  ·  07 Sep 2025  ·  doi:10.1101/2025.09.05.674557
Functional time transformation model with applications to digital health
Functional time transformation model with applications to digital health
Rahul Ghosal, Marcos Matabuena, Sujit K. Ghosh
Computational Statistics & Data Analysis  ·  01 Jul 2025  ·  doi:10.1016/j.csda.2025.108131
Model-Free Kernel Conformal Depth Measures Algorithm for Uncertainty Quantification in Regression Models in Separable Hilbert Spaces
Model-Free Kernel Conformal Depth Measures Algorithm for Uncertainty Quantification in Regression Models in Separable Hilbert Spaces
Marcos Matabuena, Rahul Ghosal, Pavlo Mozharovskyi, Oscar Hernan Madrid Padilla, Jukka-Pekka Onnela
arXiv  ·  11 Jun 2025  ·  arxiv:2506.08325
Conformal prediction for uncertainty quantification in dynamic biological systems
Conformal prediction for uncertainty quantification in dynamic biological systems
Alberto Portela, Julio R. Banga, Marcos Matabuena
PLOS Computational Biology  ·  12 May 2025  ·  doi:10.1371/journal.pcbi.1013098
A Telehealth Program Using Continuous Glucose Monitoring and a Connected Insulin Pen Cap in Nursing Homes for Older Adults with Insulin-Treated Diabetes: The Trescasas Study
A Telehealth Program Using Continuous Glucose Monitoring and a Connected Insulin Pen Cap in Nursing Homes for Older Adults with Insulin-Treated Diabetes: The Trescasas Study
Fernando Gomez-Peralta, Cristina Abreu, Estefanía Santos, Alvaro Da Silva, Ana San Frutos, …, Ana Franco-López, Cristina López Mardomingo, Benito Cañuelo, Guillermo Blazquez, Marcos Matabuena
Diabetes Technology & Therapeutics  ·  01 May 2025  ·  doi:10.1089/dia.2024.0356
Uncertainty quantification for intervals
Uncertainty quantification for intervals
Carlos García Meixide, Michael R. Kosorok, Marcos Matabuena
arXiv  ·  01 Apr 2025  ·  arxiv:2408.16381
Deep Learning Framework with Uncertainty Quantification for Survey Data: Assessing and Predicting Diabetes Mellitus Risk in the American Population
Deep Learning Framework with Uncertainty Quantification for Survey Data: Assessing and Predicting Diabetes Mellitus Risk in the American Population
Marcos Matabuena, Juan C. Vidal, Rahul Ghosal, Jukka-Pekka Onnela
arXiv  ·  27 Mar 2025  ·  arXiv:2403.19752
Optimal Cut-Point Estimation for Functional Digital Biomarkers: Application to Diabetes Risk Stratification via Continuous Glucose Monitoring
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, Marcos Matabuena
arXiv  ·  11 Mar 2025  ·  arxiv:2404.09716
Causal survival embeddings: Non-parametric counterfactual inference under right-censoring
Causal survival embeddings: Non-parametric counterfactual inference under right-censoring
Carlos García Meixide, Marcos Matabuena
Statistical Methods in Medical Research  ·  11 Feb 2025  ·  doi:10.1177/09622802241311455
Kernel biclustering algorithm in Hilbert spaces
Kernel biclustering algorithm in Hilbert spaces
Marcos Matabuena, Juan C. Vidal, Oscar Hernan Madrid Padilla, Dino Sejdinovic
Advances in Data Analysis and Classification  ·  21 Jan 2025  ·  doi:10.1007/s11634-024-00619-0
Variable Selection Methods for Multivariate, Functional, and Complex Biomedical Data in the AI Age
Variable Selection Methods for Multivariate, Functional, and Complex Biomedical Data in the AI Age
Marcos Matabuena
arXiv  ·  14 Jan 2025  ·  arxiv:2501.06868

2024

Predicting distributions of physical activity profiles in the National Health and Nutrition Examination Survey database using a partially linear Fr chet single index model
Predicting distributions of physical activity profiles in the National Health and Nutrition Examination Survey database using a partially linear Fréchet single index model
Marcos Matabuena, Aritra Ghosal, Wendy Meiring, Alexander Petersen
Biostatistics  ·  31 Dec 2024  ·  doi:10.1093/biostatistics/kxaf013
Divergent hypoglycemic and hyperglycemic responses to the components of evening meals. A general adult population study in individuals without diabetes AEGIS Study
Divergent hypoglycemic and hyperglycemic responses to the components of evening meals. A general adult population study in individuals without diabetes (AEGIS Study)
Tomás González-Vidal, Mar Calvo-Malvar, Carmen Fernández-Merino, Juan Sánchez-Castro, Óscar Lado-Baleato, Carla Díaz-Louzao, Marcos Pazos-Couselo, Manuela Alonso-Sampedro, Marcos Matabuena, Francisco Gude
Clinical Nutrition  ·  01 Dec 2024  ·  doi:10.1016/j.clnu.2024.11.020
Exploring the Interplay Between Physical Activity, Dietary Patterns, Diabetes, and All-Cause Mortality in the U.S.A. Population, NHANES 2011-2014: A Distributional Data Analysis Perspective
Exploring the Interplay Between Physical Activity, Dietary Patterns, Diabetes, and All-Cause Mortality in the U.S.A. Population, NHANES 2011-2014: A Distributional Data Analysis Perspective
Marcos Matabuena, Anxo Carreira, Robert Wagner, Miguel Sousa, Miguel Saavedra, Rahul Ghosal, JP Onnela, Lucimere Bohn
Springer Science and Business Media LLC  ·  15 Nov 2024  ·  doi:10.21203/rs.3.rs-5314139/v1
Exploring the association between daily distributional patterns of physical activity and cardiovascular mortality risk among older adults in NHANES 2003-2006
Exploring the association between daily distributional patterns of physical activity and cardiovascular mortality risk among older adults in NHANES 2003-2006
Sunwoo Emma Cho, Enakshi Saha, Marcos Matabuena, Jingkai Wei, Rahul Ghosal
Annals of Epidemiology  ·  01 Nov 2024  ·  doi:10.1016/j.annepidem.2024.10.001
Uncertainty Quantification in Dynamic Models of Biological Systems Using Conformal Prediction
Uncertainty Quantification in Dynamic Models of Biological Systems Using Conformal Prediction
Alberto Portela, Julio R. Banga, Marcos Matabuena
[object Object]  ·  01 Oct 2024  ·  doi: 10.5281/zenodo.13838652
Book Review: Functional Data Analysis with R by Ciprian M. Crainiceanu, Jeff Goldsmith, Andrew Leroux, and Erjia Cui
Book Review: Functional Data Analysis with R by Ciprian M. Crainiceanu, Jeff Goldsmith, Andrew Leroux, and Erjia Cui
Marcos Matabuena
Journal of Agricultural, Biological and Environmental Statistics  ·  01 Oct 2024  ·  doi:10.1007/s13253-024-00656-4
Multivariate scalar on multidimensional distribution regression with application to modeling the association between physical activity and cognitive functions
Multivariate scalar on multidimensional distribution regression with application to modeling the association between physical activity and cognitive functions
Rahul Ghosal, Marcos Matabuena
Biometrical Journal  ·  22 Sep 2024  ·  doi:10.1002/bimj.202400042
Neural interval-censored survival regression with feature selection
Neural interval-censored survival regression with feature selection
Carlos García Meixide, Marcos Matabuena, Louis Abraham, Michael R. Kosorok
Statistical Analysis and Data Mining: The ASA Data Science Journal  ·  16 Jul 2024  ·  doi:10.1002/sam.11704
Energy Distance and Kernel Mean Embeddings for Two-Sample Survival Testing with Applications in Immunotherapy Clinical Trials
Energy Distance and Kernel Mean Embeddings for Two-Sample Survival Testing with Applications in Immunotherapy Clinical Trials
Marcos Matabuena, Oscar Hernan Madrid Padilla
REVSTAT-Statistical Journal  ·  14 Jun 2024  ·  doi:10.57805/revstat.vi.572
Conformal uncertainty quantification using kernel depth measures in separable Hilbert spaces
Conformal uncertainty quantification using kernel depth measures in separable Hilbert spaces
Marcos Matabuena, Rahul Ghosal, Pavlo Mozharovskyi, Oscar Hernan Madrid Padilla, Jukka-Pekka Onnela
arXiv  ·  24 May 2024  ·  arxiv:2405.13970
Uncertainty quantification in metric spaces
Uncertainty quantification in metric spaces
Gábor Lugosi, Marcos Matabuena
arXiv  ·  09 May 2024  ·  arxiv:2405.05110
Insulin glargine 300 U ml versus insulin degludec 100 U ml improves nocturnal glycaemic control and variability in type 1 diabetes under routine clinical practice: A glucodensities-based post hoc analysis of the OneCare study
Insulin glargine 300 U/ml versus insulin degludec 100 U/ml improves nocturnal glycaemic control and variability in type 1 diabetes under routine clinical practice: A glucodensities-based post hoc analysis of the OneCare study
Fernando Gomez‐Peralta, Ana Chico Ballesteros, Amparo Marco Martínez, Begoña Pérez Corral, Ignacio Conget Donlo, Paulina Fuentealba Melo, Fernando Zaragozá Arnáez, Marcos Matabuena Rodríguez
Diabetes, Obesity and Metabolism  ·  01 May 2024  ·  doi: 10.1111/dom.15496
Personalized Imputation in metric spaces via conformal prediction: Applications in Predicting Diabetes Development with Continuous Glucose Monitoring Information
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, Francisco Gude
arXiv  ·  28 Mar 2024  ·  arxiv:2403.18069
Insulin glargine 300 U ml versus insulin degludec 100 U ml improves nocturnal glycaemic control and variability in type 1 diabetes under routine clinical practice: A glucodensities-based post hoc analysis of the OneCare study
Insulin glargine 300 U/ml versus insulin degludec 100 U/ml improves nocturnal glycaemic control and variability in type 1 diabetes under routine clinical practice: A glucodensities-based post hoc analysis of the OneCare study
Fernando Gomez‐Peralta, Ana Chico Ballesteros, Amparo Marco Martínez, Begoña Pérez Corral, Ignacio Conget Donlo, Paulina Fuentealba Melo, Fernando Zaragozá Arnáez, Marcos Matabuena Rodríguez
Diabetes, Obesity and Metabolism  ·  20 Feb 2024  ·  doi:10.1111/dom.15496
Author Correction: Reassessment of fluctuating dental asymmetry in Down syndrome
Author Correction: Reassessment of fluctuating dental asymmetry in Down syndrome
Marcos Matabuena Rodríguez, Pedro Diz Dios, Carmen Cadarso-Suárez, Márcio Diniz-Freitas, Mercedes Outumuro Rial, Maria Teresa Abeleira Pazos, Jacobo Limeres Posse
Scientific Reports  ·  16 Jan 2024  ·  doi:10.1038/s41598-024-51672-w

2021

Glucodensities: A new representation of glucose profiles using distributional data analysis
Glucodensities: A new representation of glucose profiles using distributional data analysis
Marcos Matabuena, Alexander Petersen, Juan C Vidal, Francisco Gude
Statistical Methods in Medical Research  ·  01 Jun 2021  ·  doi: 10.1177/0962280221998064