BMSS

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.


References in zbMATH (referenced in 11 articles )

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  1. Cai, Qingpo; Kang, Jian; Yu, Tianwei: Bayesian network marker selection via the thresholded graph Laplacian Gaussian prior (2020)
  2. Dobra, Adrian; Valdes, Camilo; Ajdic, Dragana; Clarke, Bertrand; Clarke, Jennifer: Modeling association in microbial communities with clique loglinear models (2019)
  3. He, Hongjin; Xu, Hong-Kun: Splitting methods for split feasibility problems with application to Dantzig selectors (2017)
  4. Li, Guoyin; Liu, Tianxiang; Pong, Ting Kei: Peaceman-Rachford splitting for a class of nonconvex optimization problems (2017)
  5. Papathomas, Michail; Richardson, Sylvia: Exploring dependence between categorical variables: benefits and limitations of using variable selection within Bayesian clustering in relation to log-linear modelling with interaction terms (2016)
  6. Hadiji, Fabian; Molina, Alejandro; Natarajan, Sriraam; Kersting, Kristian: Poisson dependency networks: gradient boosted models for multivariate count data (2015)
  7. He, Hongjin; Cai, Xingju; Han, Deren: A fast splitting method tailored for Dantzig selector (2015)
  8. Ravkic, Irma; Ramon, Jan; Davis, Jesse: Learning relational dependency networks in hybrid domains (2015)
  9. Lamnisos, D.; Griffin, J. E.; Steel, M. F. J.: Cross-validation prior choice in Bayesian probit regression with many covariates (2012)
  10. Lu, Zhaosong; Pong, Ting Kei; Zhang, Yong: An alternating direction method for finding Dantzig selectors (2012)
  11. Li, Lu; Toh, Kim-Chuan: An inexact interior point method for (L_1)-regularized sparse covariance selection (2010)