Nonparametric quantile scalar-on-image regression

Chuchu Wang and Xinyuan Song

Computational Statistics & Data Analysis, 2024, vol. 191, issue C

Abstract: A quantile scalar-on-image regression model is developed to comprehensively study the relationship between cognitive decline and various clinical covariates and imaging factors. As a motivating example, the high-dimensional brain imaging data from the research on Alzheimer's disease are considered predictors of patients' cognitive decline. A Bayesian nonparametric model is proposed to handle the complex spatially distributed imaging data, where the coefficient function is assumed to be a latent Gaussian process. A soft-thresholding operator is introduced to capture the sparse structure of the regression coefficients. Utilizing kernel basis functions to approximate the latent Gaussian process facilitates easy-to-implement computation and consistent estimation. Inference is performed within the Bayesian framework, using an efficient Markov chain Monte Carlo algorithm. The proposed method is compared with the functional principal component analysis method in simulations and applied to a study of Alzheimer's disease.

Keywords: Bayesian approach; Gaussian process; Imaging data; Quantile regression; Soft-thresholding operator (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0167947323001846
Full text for ScienceDirect subscribers only.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:191:y:2024:i:c:s0167947323001846

DOI: 10.1016/j.csda.2023.107873

Access Statistics for this article

Computational Statistics & Data Analysis is currently edited by S.P. Azen

More articles in Computational Statistics & Data Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().