Work

Bayesian Hierarchical Model for Large-Scale Covariance Matrix Estimation

Year: 2007

Type: article

Abstract: Many bioinformatics problems implicitly depend on estimating large-scale covariance matrix. The traditional approaches tend to give rise to high variance and low accuracy due to "overfitting." We cast the large-scale covariance matrix estimation problem into the Bayesian hierarchical model framework, and introduce dependency between covariance parameters. We demonstrate the advantages of our approaches over the traditional approaches using simulations and OMICS data analysis.

Source: Journal of Computational Biology

DOI: 10.1089/cmb.2006.0151

Authors: Dongxiao Zhu, Alfred O. Hero III

Institutions: Stowers Institute for Medical Research, University of Kansas Medical Center, University of Michigan

Language: English

Topic: Statistical Methods and Inference

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W2025450578

FWCI: 1.87

Cites: 20

Cited by: 13

Related to: 10