Zobrazeno 1 - 10
of 127
pro vyhledávání: '"Matabuena, Marcos"'
The advent of wearable and sensor technologies now leads to functional predictors which are intrinsically infinite dimensional. While the existing approaches for functional data and survival outcomes lean on the well-established Cox model, the propor
Externí odkaz:
http://arxiv.org/abs/2406.19716
Glucose meal response information collected via Continuous Glucose Monitoring (CGM) is relevant to the assessment of individual metabolic status and the support of personalized diet prescriptions. However, the complexity of the data produced by CGM m
Externí odkaz:
http://arxiv.org/abs/2405.14690
Autor:
Matabuena, Marcos, Ghosal, Rahul, Mozharovskyi, Pavlo, Padilla, Oscar Hernan Madrid, Onnela, Jukka-Pekka
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 their versatility, integrating depth measures into regression modeling
Externí odkaz:
http://arxiv.org/abs/2405.13970
Autor:
Lugosi, Gábor, Matabuena, Marcos
This paper introduces a novel uncertainty quantification framework for regression models where the response takes values in a separable metric space, and the predictors are in a Euclidean space. The proposed algorithms can efficiently handle large da
Externí odkaz:
http://arxiv.org/abs/2405.05110
Establish optimal cut points plays a crucial role in epidemiology and biomarker discovery, enabling the development of effective and practical clinical decision criteria. While there is extensive literature to define optimal cut off over scalar bioma
Externí odkaz:
http://arxiv.org/abs/2404.09716
Complex survey designs are commonly employed in many medical cohorts. In such scenarios, developing case-specific predictive risk score models that reflect the unique characteristics of the study design is essential. This approach is key to minimizin
Externí odkaz:
http://arxiv.org/abs/2403.19752
The challenge of handling missing data is widespread in modern data analysis, particularly during the preprocessing phase and in various inferential modeling tasks. Although numerous algorithms exist for imputing missing data, the assessment of imput
Externí odkaz:
http://arxiv.org/abs/2403.18069
Continuous glucose monitoring (CGM) is a minimally invasive technology that allows continuous monitoring of an individual's blood glucose. We focus on a large clinical trial that collected CGM data every few minutes for 26 weeks and assumes that the
Externí odkaz:
http://arxiv.org/abs/2403.10514
In this paper, we introduce a kNN-based regression method that synergizes the scalability and adaptability of traditional non-parametric kNN models with a novel variable selection technique. This method focuses on accurately estimating the conditiona
Externí odkaz:
http://arxiv.org/abs/2402.01635
Autor:
Ghosal, Rahul, Matabuena, Marcos
We develop a new method for multivariate scalar on multidimensional distribution regression. Traditional approaches typically analyze isolated univariate scalar outcomes or consider unidimensional distributional representations as predictors. However
Externí odkaz:
http://arxiv.org/abs/2310.10494