Fully Bayesian T-probit Regression with Heavy-tailed Priors for Selection in High-Dimensional Features with Grouping Structure

Fully Bayesian T-probit Regression with Heavy-tailed Priors for Selection in High-Dimensional Features with Grouping Structure

Feature selection is demanded in many modern scientific research problems that use high-dimensional data. A typical example is to find the genes that are most related to a certain disease (e.g., cancer) from high-dimensional gene expression profiles. There are tremendous difficulties in eliminating...

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Bibliographic Details
Other Authors: Li, Longhai
Language:English
Published: 2015
Subjects:
Bayesian methods
probit
MCMC
gene expression data
grouping structure
Online Access:http://hdl.handle.net/10388/ETD-2015-09-2232

Internet

http://hdl.handle.net/10388/ETD-2015-09-2232

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