Journal
BIOSTATISTICS
Volume 10, Issue 4, Pages 621-639Publisher
OXFORD UNIV PRESS
DOI: 10.1093/biostatistics/kxp018
Keywords
Bayesian regression analysis; Dependency networks; Gene expression; Stochastic search; Variable selection
Funding
- NHLBI NIH HHS [R01 HL092071] Funding Source: Medline
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We describe a new stochastic search algorithm for linear regression models called the bounded mode stochastic search (BMSS). We make use of BMSS to perform variable selection and classification as well as to construct sparse dependency networks. Furthermore, we show how to determine genetic networks from genomewide data that involve any combination of continuous and discrete variables. We illustrate our methodology with several real-world data sets.
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