Bayesian penalized model for classification and selection of functional predictors using longitudinal MRI data from ADNI-Reference-Cited by-同舟云学术

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

Published:2022-05-09 Issue:4 Volume:6 Page:327-343
ISSN:2475-4269
Container-title:Statistical Theory and Related Fields
language:en
Short-container-title:Statistical Theory and Related Fields

Author:

Banik Asish¹^ORCID,Maiti Taps¹,Bender Andrew²

Affiliation:

1. Department of Statistics & Probability, Michigan State University, East Lansing, MI, USA

2. Department of Epidemiology & Biostatistics, Department of Neurology & Ophthalmology, Michigan State University, East Lansing, MI, USA

Funder

Directorate for Mathematical and Physical Sciences

Publisher

Informa UK Limited

Subject

Applied Mathematics,Computational Theory and Mathematics,Statistics, Probability and Uncertainty,Statistics and Probability,Analysis

Link

https://www.tandfonline.com/doi/pdf/10.1080/24754269.2022.2064611

Reference36 articles.

1. How early can we predict Alzheimer's disease using computational anatomy?

2. Association between fully automated MRI-based volumetry of different brain regions and neuropsychological test performance in patients with amnestic mild cognitive impairment and Alzheimer’s disease

3. High Dimensional Classification of Structural MRI Alzheimer?s Disease Data Based on Large Scale Regularization

4. Penalized regression, standard errors, and Bayesian lassos

5. Spatial patterns of brain atrophy in MCI patients, identified via high-dimensional pattern classification, predict subsequent cognitive decline

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