Scientific interests
Functional data analysis and kernel methods.
Distributional data analysis in werable data analysis.
Uncertainty quantification: Conformal Inference.
Statistical analysis in metric spaces
Causal inference in digital medicine
Survival analysis with machine learning models
Missing and survey data
Empirical process theory and U-statistics
Relevant applications in digital and personalized medicine, diabetes, and physical activity
Multilevel models and reliability analysis
Relevant Publications
Matabuena, M., Vidal, J. C., Padilla, O. H. M., & Sejdinovic, D. (2022). Kernel Biclusteringalgorithm in Hilbert Spaces. arXiv preprint arXiv:2208.03675.
Matabuena, M., Petersen, A., Vidal, J. C., & Gude, F. (2021). Glucodensities: A new representation of glucose profiles using distributional data analysis. Statistical methods in medical research, 0962280221998064.
Meixide, Carlos García; Matabuena, Marcos; Kosorok, Michael R. Neural interval-censored Cox regression with feature selection. arXiv preprint arXiv:2206.06885, 2022.
Matabuena, M., & Ditzhaus M. Félix P, Vidal J.C, Gude .F Hypothesis testing in the presence of complex paired missing data by maximum mean discrepancy: An application to continuous glucose monitoring. American Statistician.
Matabuena, M., Karas, M., Riazati, S., Caplan, N., & Hayes, P. R. (2022). Estimating KneeMovement Patterns of Recreational Runners Across Training Sessions Using Multilevel Functional Regression Models. The American Statistician, 1-13.
Matabuena, M., Rodríguez-Mier, P., García-Meixide, C., & Leborán, V. (2021). COVID-19:Estimation of the transmission dynamics in Spain using a stochastic simulator and black-box optimization techniques. Computer methods and programs in biomedicine, 211, 106399.
Matabuena, M., Vidal, J. C., Hayes, P. R., & Huelin Trillo, F. (2018). A 6-minute sub-maximalrun test to predict VO2 max. Journal of sports sciences, 36(22), 2531-2536.
Ghosal, A.,Matabuena, M.¹, Meiring, W., Petersen, A. (2023). Predicting distributional profiles of physical activity in the NHANES database using a Partially Linear Single-IndexFréchet Regression model. arXiv preprint arXiv:2302.07692.
Matabuena, M., Félix, P., Meixide, C., & Gude, F. (2022). . Kernel machine learning methods to handle missing responses with complex predictors. Application in modelling five-year glucose changes using distributional representations. Computer Methods and Programs in Biomedicine
Matabuena, M., & Rodríguez-López, R. (2019). An Improved Version of the Classical Banis-ter Model to Predict Changes in Physical Condition. Bulletin of mathematical biology, 81(6),1867-1884.
Some researchers with whom I have had the privilege of working
Discovering, constructing, and nurturing scientific collaborators holds immense significance. My exceptional collaborators have played a pivotal role in shaping my thoughts, advancing my professional journey, and influencing the problems I choose to tackle. Collaborating with distinguished scientists worldwide not only brings immense satisfaction but also opens extraordinary avenues to make a profound difference in the lives of millions.