Zobrazeno 1 - 10
of 598
pro vyhledávání: '"Vannucci, Marina"'
Covariate-dependent graph learning has gained increasing interest in the graphical modeling literature for the analysis of heterogeneous data. This task, however, poses challenges to modeling, computational efficiency, and interpretability. The param
Externí odkaz:
http://arxiv.org/abs/2409.17404
Count data play a crucial role in sports analytics, providing valuable insights into various aspects of the game. Models that accurately capture the characteristics of count data are essential for making reliable inferences. In this paper, we propose
Externí odkaz:
http://arxiv.org/abs/2409.17129
Motivated by an increasing demand for models that can effectively describe features of complex multivariate time series, e.g. from sensor data in biomechanics, motion analysis, and sports science, we introduce a novel state-space modeling framework w
Externí odkaz:
http://arxiv.org/abs/2407.20085
In this paper, we propose Varying Effects Regression with Graph Estimation (VERGE), a novel Bayesian method for feature selection in regression. Our model has key aspects that allow it to leverage the complex structure of data sets arising from genom
Externí odkaz:
http://arxiv.org/abs/2407.05089
Autor:
Ricci, Federica Zoe, Sudderth, Erik B., Lee, Jaylen, Peters, Megan A. K., Vannucci, Marina, Guindani, Michele
We consider the problem of analyzing multivariate time series collected on multiple subjects, with the goal of identifying groups of subjects exhibiting similar trends in their recorded measurements over time as well as time-varying groups of associa
Externí odkaz:
http://arxiv.org/abs/2406.17131
We propose a flexible Bayesian approach for sparse Gaussian graphical modeling of multivariate time series. We account for temporal correlation in the data by assuming that observations are characterized by an underlying and unobserved hidden discret
Externí odkaz:
http://arxiv.org/abs/2406.03385
Functional concurrent, or varying-coefficient, regression models are commonly used in biomedical and clinical settings to investigate how the relation between an outcome and observed covariate varies as a function of another covariate. In this work,
Externí odkaz:
http://arxiv.org/abs/2405.11358
Functional data analysis, which models data as realizations of random functions over a continuum, has emerged as a useful tool for time series data. Often, the goal is to infer the dynamic connections (or time-varying conditional dependencies) among
Externí odkaz:
http://arxiv.org/abs/2405.03041
Autor:
Ren, Yangfan, Osborne, Nathan, Peterson, Christine B., DeMaster, Dana M., Ewing-Cobbs, Linda, Vannucci, Marina
Publikováno v:
Human Brain Mapping (2024), 45(10), e26763
In this paper, we develop an analytical approach for estimating brain connectivity networks that accounts for subject heterogeneity. More specifically, we consider a novel extension of a multi-subject Bayesian vector autoregressive model that estimat
Externí odkaz:
http://arxiv.org/abs/2405.00535
Tensor-based representations are being increasingly used to represent complex data types such as imaging data, due to their appealing properties such as dimension reduction and the preservation of spatial information. Recently, there is a growing lit
Externí odkaz:
http://arxiv.org/abs/2312.08587