Variable selection and dependency networks for genomewide data. 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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References in zbMATH (referenced in 8 articles )
Showing results 1 to 8 of 8.
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- He, Hongjin; Cai, Xingju; Han, Deren: A fast splitting method tailored for Dantzig selector (2015)
- Ravkic, Irma; Ramon, Jan; Davis, Jesse: Learning relational dependency networks in hybrid domains (2015)
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- Lu, Zhaosong; Pong, Ting Kei; Zhang, Yong: An alternating direction method for finding Dantzig selectors (2012)
- Li, Lu; Toh, Kim-Chuan: An inexact interior point method for $L_1$-regularized sparse covariance selection (2010)