Zobrazeno 1 - 10
of 731
pro vyhledávání: '"P. Onnela"'
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
Autor:
Le, Thien-Minh, Onnela, Jukka-Pekka
Infectious disease modeling is used to forecast epidemics and assess the effectiveness of intervention strategies. Although the core assumption of mass-action models of homogeneously mixed population is often implausible, they are nevertheless routin
Externí odkaz:
http://arxiv.org/abs/2408.15353
Speech emotion recognition (SER) systems often struggle in real-world environments, where ambient noise severely degrades their performance. This paper explores a novel approach that exploits prior knowledge of testing environments to maximize SER pe
Externí odkaz:
http://arxiv.org/abs/2407.17716
Autor:
Cai, Xiaoxuan, Zeng, Li, Fowler, Charlotte, Dixon, Lisa, Ongur, Dost, Baker, Justin T., Onnela, Jukka-Pekka, Valeri, Linda
Mobile technology (mobile phones and wearable devices) generates continuous data streams encompassing outcomes, exposures and covariates, presented as intensive longitudinal or multivariate time series data. The high frequency of measurements enables
Externí odkaz:
http://arxiv.org/abs/2407.17666
We introduce a family of parsimonious network models that are intended to generalize the configuration model to temporal settings. We present consistent estimators for the model parameters and perform numerical simulations to illustrate the propertie
Externí odkaz:
http://arxiv.org/abs/2407.12175
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:
Wang, Maxwell H., Onnela, Jukka-Pekka
When modeling the dynamics of infectious disease, the incorporation of contact network information allows for the capture of the non-randomness and heterogeneity of realistic contact patterns. Oftentimes, it is assumed that the underlying contact pat
Externí odkaz:
http://arxiv.org/abs/2404.02924
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
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
Network models are increasingly used to study infectious disease spread. Exponential Random Graph models have a history in this area, with scalable inference methods now available. An alternative approach uses mechanistic network models. Mechanistic
Externí odkaz:
http://arxiv.org/abs/2401.04775