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pro vyhledávání: '"Eloyan A"'
Longitudinal magnetic resonance imaging data is used to model trajectories of change in brain regions of interest to identify areas susceptible to atrophy in those with neurodegenerative conditions like Alzheimer's disease. Most methods for extractin
Externí odkaz:
http://arxiv.org/abs/2407.17450
Brain functional connectivity (FC), the temporal synchrony between brain networks, is essential to understand the functional organization in the brain and to identify changes due to neurological disorders, development, treatment, and other phenomena.
Externí odkaz:
http://arxiv.org/abs/2311.03791
Autor:
Meng, Kun, Ji, Mattie, Wang, Jinyu, Ding, Kexin, Kirveslahti, Henry, Eloyan, Ani, Crawford, Lorin
Tools from topological data analysis have been widely used to represent binary images in many scientific applications. Methods that aim to represent grayscale images (i.e., where pixel intensities instead take on continuous values) have been relative
Externí odkaz:
http://arxiv.org/abs/2308.14249
Motivated by the need for computationally tractable spatial methods in neuroimaging studies, we develop a distributed and integrated framework for estimation and inference of Gaussian process model parameters with ultra-high-dimensional likelihoods.
Externí odkaz:
http://arxiv.org/abs/2305.15951
Publikováno v:
Journal of the American Statistical Association, 2024
In this article, we establish the mathematical foundations for modeling the randomness of shapes and conducting statistical inference on shapes using the smooth Euler characteristic transform. Based on these foundations, we propose two chi-squared st
Externí odkaz:
http://arxiv.org/abs/2204.12699
Functional connectivity (FC) refers to the investigation of interactions between brain regions to understand integration of neural activity in several regions. FC is often estimated using functional magnetic resonance images (fMRI). There has been in
Externí odkaz:
http://arxiv.org/abs/2111.08118
Autor:
Eloyan, Ani, Rose, Sherri
Publikováno v:
Observational Studies (2021); 7(1):191-196. https://muse.jhu.edu/article/799734
We consider an extension of Leo Breiman's thesis from "Statistical Modeling: The Two Cultures" to include a bifurcation of algorithmic modeling, focusing on parametric regressions, interpretable algorithms, and complex (possibly explainable) algorith
Externí odkaz:
http://arxiv.org/abs/2104.06571
Autor:
Meng, Kun, Eloyan, Ani
Publikováno v:
Journal of the Royal Statistical Society Series C: Applied Statistics, 2024;, qlae015
Functional magnetic resonance imaging (fMRI) is a non-invasive and in-vivo imaging technique essential for measuring brain activity. Functional connectivity is used to study associations between brain regions, either while study subjects perform task
Externí odkaz:
http://arxiv.org/abs/2102.12039
Autor:
Meng, Kun1 (AUTHOR) kun_meng@brown.edu, Eloyan, Ani2 (AUTHOR)
Publikováno v:
Journal of the Royal Statistical Society: Series C (Applied Statistics). Aug2024, Vol. 73 Issue 4, p857-879. 23p.
Autor:
Meng, Kun, Eloyan, Ani
Publikováno v:
Journal of the Royal Statistical Society: Series B (Statistical Methodology), 2021
We propose a framework of principal manifolds to model high-dimensional data. This framework is based on Sobolev spaces and designed to model data of any intrinsic dimension. It includes principal component analysis and principal curve algorithm as s
Externí odkaz:
http://arxiv.org/abs/1711.06746