Bayesian predictive modeling of multi-source multi-way data.

Autor: Kim J; Division of Biostatistics, University of Minnesota, Minneapolis, 55455, USA., Sandri BJ; Division of Neonatology, Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA.; Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA., Rao RB; Division of Neonatology, Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA.; Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN, USA., Lock EF; Division of Biostatistics, University of Minnesota, Minneapolis, 55455, USA.
Jazyk: angličtina
Zdroj: Computational statistics & data analysis [Comput Stat Data Anal] 2023 Oct; Vol. 186. Date of Electronic Publication: 2023 May 19.
DOI: 10.1016/j.csda.2023.107783
Abstrakt: A Bayesian approach to predict a continuous or binary outcome from data that are collected from multiple sources with a multi-way (i.e., multidimensional tensor) structure is described. As a motivating example, molecular data from multiple 'omics sources, each measured over multiple developmental time points, as predictors of early-life iron deficiency (ID) in a rhesus monkey model are considered. The method uses a linear model with a low-rank structure on the coefficients to capture multi-way dependence and model the variance of the coefficients separately across each source to infer their relative contributions. Conjugate priors facilitate an efficient Gibbs sampling algorithm for posterior inference, assuming a continuous outcome with normal errors or a binary outcome with a probit link. Simulations demonstrate that the model performs as expected in terms of misclassification rates and correlation of estimated coefficients with true coefficients, with large gains in performance by incorporating multi-way structure and modest gains when accounting for differing signal sizes across the different sources. Moreover, it provides robust classification of ID monkeys for the motivating application.
Databáze: MEDLINE