Bayesian penalized model for classification and selection of functional predictors using longitudinal MRI data from ADNI

The main goal of this paper is to employ longitudinal trajectories in a significant number of sub-regional brain volumetric MRI data as statistical predictors for Alzheimer's disease (AD) classification. We use logistic regression in a Bayesian framework that includes many functional predictors. The...

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Dettagli Bibliografici

Autori principali:	Asish Banik, Taps Maiti, Andrew Bender
Natura:	Artigo
Lingua:	Inglês
Pubblicazione:	Taylor & Francis Group 2022-11-01
Serie:	Statistical Theory and Related Fields
Soggetti:	alzheimer's disease basis spline pólya-gamma augmentation bayesian group lasso spike-and-slab prior gibbs sampler volumetric mri adni
Accesso online:	http://dx.doi.org/10.1080/24754269.2022.2064611
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