Zobrazeno 1 - 10
of 109
pro vyhledávání: '"GHOSAL, RAHUL"'
Autor:
Matabuena, Marcos, Ghosal, Rahul, Aguilar, Javier Enrique, Wagner, Robert, Merino, Carmen Fernández, Castro, Juan Sánchez, Zipunnikov, Vadim, Onnela, Jukka-Pekka, Gude, Francisco
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 hypoglycemia. Recently, there has been growing interest in utilizing CG
Externí odkaz:
http://arxiv.org/abs/2410.00912
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
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
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
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
Mobile health studies often collect multiple within-day self-reported assessments of participants' behavior and well-being on different scales such as physical activity (continuous), pain levels (truncated), mood states (ordinal), and life events (bi
Externí odkaz:
http://arxiv.org/abs/2306.15084
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 populations with a cure fraction of a particular event of interest is e
Externí odkaz:
http://arxiv.org/abs/2302.07340
Modern clinical and epidemiological studies widely employ wearables to record parallel streams of real-time data on human physiology and behavior. With recent advances in distributional data analysis, these high-frequency data are now often treated a
Externí odkaz:
http://arxiv.org/abs/2301.11399
Shape restrictions on functional regression coefficients such as non-negativity, monotonicity, convexity or concavity are often available in the form of a prior knowledge or required to maintain a structural consistency in functional regression model
Externí odkaz:
http://arxiv.org/abs/2209.04476