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Volumn 8, Issue 3, 2014, Pages 1750-1781

Variable selection for bart: An application to gene regulation

Author keywords

Bayesian learning; Decision trees; Gene regulatory network; Machine learning; Nonparametric regression; Permutation testing; Variable selection

Indexed keywords


EID: 84908274658     PISSN: 19326157     EISSN: 19417330     Source Type: Journal    
DOI: 10.1214/14-AOAS755     Document Type: Article
Times cited : (129)

References (36)
  • 1
    • 84908274658 scopus 로고    scopus 로고
    • Supplement to “Variable selection for BART: An application to gene regulation.”
    • BLEICH, J., KAPELNER, A., GEORGE, E. and Jensen S. (2014). Supplement to “Variable selection for BART: An application to gene regulation.” DOI:10.1214/14-AOAS755SUPP.
    • (2014)
    • Bleich, J.1    Kapelner, A.2    George, E.3    Jensen, S.4
  • 2
    • 79951540621 scopus 로고    scopus 로고
    • Evolutionary stochastic search for Bayesian model exploration
    • MR2719668
    • BOTTOLO, L. and RICHARDSON, S. (2010). Evolutionary stochastic search for Bayesian model exploration. Bayesian Anal. 5 583-618. MR2719668
    • (2010) Bayesian Anal , vol.5 , pp. 583-618
    • Bottolo, L.1    Richardson, S.2
  • 3
    • 0035478854 scopus 로고    scopus 로고
    • Random forests
    • BREIMAN, L. (2001). Random forests. Machine Learning 45 5-32.
    • (2001) Machine Learning , vol.45 , pp. 5-32
    • Breiman, L.1
  • 6
    • 84870288271 scopus 로고    scopus 로고
    • BART: Bayesian additive regression trees
    • MR2758172
    • CHIPMAN, H. A., GEORGE, E. I. and MCCULLOCH, R. E. (2010). BART: Bayesian additive regression trees. Ann. Appl. Stat. 4 266-298. MR2758172
    • (2010) Ann. Appl. Stat , vol.4 , pp. 266-298
    • Chipman, H.A.1    George, E.I.2    McCulloch, R.E.3
  • 7
    • 84865067900 scopus 로고    scopus 로고
    • In The 2012 International Joint Conference on Neural Networks (IJCNN)
    • DENG, H. and RUNGER, G. (2012). Feature selection via regularized trees. In The 2012 International Joint Conference on Neural Networks (IJCNN).
    • (2012) Feature selection via regularized trees
    • Deng, H.1    Runger, G.2
  • 8
    • 30644464444 scopus 로고    scopus 로고
    • Gene selection and classification of microarray data using random forest
    • Díaz-uriarte, R. And Alvarez De Andrés, S. (2006). Gene selection and classification of microarray data using random forest. BMC Bioinformatics 7 1-13.
    • (2006) BMC Bioinformatics , vol.7 , pp. 1-13
    • Díaz-uriarte, R.A.A.D.A.1
  • 9
    • 0002432565 scopus 로고
    • Multivariate adaptive regression splines
    • With discussion and a rejoinder by the author. MR1091842
    • FRIEDMAN, J. H. (1991). Multivariate adaptive regression splines. Ann. Statist. 19 1-141. With discussion and a rejoinder by the author. MR1091842
    • (1991) Ann. Statist , vol.19 , pp. 1-141
    • Friedman, J.H.1
  • 10
    • 0037186544 scopus 로고    scopus 로고
    • Stochastic gradient boosting. Comput
    • MR1884869
    • FRIEDMAN, J. H. (2002). Stochastic gradient boosting. Comput. Statist. Data Anal. 38 367-378. MR1884869
    • (2002) Statist. Data Anal , vol.38 , pp. 367-378
    • Friedman, J.H.1
  • 11
    • 77950537175 scopus 로고    scopus 로고
    • Regularization paths for generalized linear models via coordinate descent
    • FRIEDMAN, J. H., HASTIE, T. and TIBSHIRANI, R. (2010). Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33 1-22.
    • (2010) J. Stat. Softw , vol.33 , pp. 1-22
    • Friedman, J.H.1    Hastie, T.2    Tibshirani, R.3
  • 12
    • 0021518209 scopus 로고
    • Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images
    • GEMAN, S. and GEMAN, D. (1984). Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans. Pattern Anal. Mach. Intell. 6 721-741.
    • (1984) IEEE Trans. Pattern Anal. Mach. Intell , vol.6 , pp. 721-741
    • Geman, S.1    Geman, D.2
  • 14
    • 84876056744 scopus 로고    scopus 로고
    • Variable selection and sensitivity analysis using dynamic trees, with an application to computer code performance tuning
    • MR3086410
    • GRAMACY, R. B., TADDY, M. and WILD, S. M. (2013). Variable selection and sensitivity analysis using dynamic trees, with an application to computer code performance tuning. Ann. Appl. Stat. 7 51-80. MR3086410
    • (2013) Ann. Appl. Stat , vol.7 , pp. 51-80
    • Gramacy, R.B.1    Taddy, M.2    Wild, S.M.3
  • 15
    • 34147179823 scopus 로고    scopus 로고
    • Clustering of genes into regulons using integrated modeling—COGRIM
    • GUANG, C., JENSEN, S. T. and STOECKERT, C. J. (2007). Clustering of genes into regulons using integrated modeling—COGRIM. Genome Biol. 8 R4.
    • (2007) Genome Biol , vol.8 , pp. R4
    • Guang, C.1    Jensen, S.T.2    Stoeckert, C.J.3
  • 16
    • 71249130909 scopus 로고    scopus 로고
    • Bayesian lasso regression
    • MR2564494
    • HANS, C. (2009). Bayesian lasso regression. Biometrika 96 835-845. MR2564494
    • (2009) Biometrika , vol.96 , pp. 835-845
    • Hans, C.1
  • 17
    • 34250747348 scopus 로고    scopus 로고
    • Shotgun stochastic search for “large p” regression
    • MR2370849
    • HANS, C., DOBRA, A. and WEST, M. (2007). Shotgun stochastic search for “large p” regression. J. Amer. Statist. Assoc. 102 507-516. MR2370849
    • (2007) J. Amer. Statist. Assoc , vol.102 , pp. 507-516
    • Hans, C.1    Dobra, A.2    West, M.3
  • 18
    • 77956890234 scopus 로고
    • Monte Carlo sampling methods using Markov chains and their applications
    • HASTINGS, H. K. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57 97-109.
    • (1970) Biometrika , vol.57 , pp. 97-109
    • Hastings, H.K.1
  • 19
    • 0017280570 scopus 로고
    • The analysis and selection of variables in linear regression
    • MR0398008
    • HOCKING, R. R. (1976). The analysis and selection of variables in linear regression. Biometrics 32 1-49. MR0398008
    • (1976) Biometrics , vol.32 , pp. 1-49
    • Hocking, R.R.1
  • 20
    • 22944460748 scopus 로고    scopus 로고
    • Spike and slab variable selection: Frequentist and Bayesian strategies
    • MR2163158
    • ISHWARAN, H. and RAO, J. S. (2005). Spike and slab variable selection: Frequentist and Bayesian strategies. Ann. Statist. 33 730-773. MR2163158
    • (2005) Ann. Statist , vol.33 , pp. 730-773
    • Ishwaran, H.1    Rao, J.S.2
  • 21
    • 84908273395 scopus 로고    scopus 로고
    • Generalized ridge regression: Geometry and computational solutions when p is larger than n. Technical report
    • ISHWARAN, H. and RAO, J S. (2010). Generalized ridge regression: Geometry and computational solutions when p is larger than n. Technical report.
    • (2010)
    • Ishwaran, H.1    Rao, J.S.2
  • 23
    • 46949103774 scopus 로고    scopus 로고
    • Bayesian variable selection and data integration for biological regulatory networks
    • MR2415749
    • JENSEN, S. T., CHEN, G. and STOECKERT, C. J., JR. (2007). Bayesian variable selection and data integration for biological regulatory networks. Ann. Appl. Stat. 1 612-633. MR2415749
    • (2007) Ann. Appl. Stat , vol.1 , pp. 612-633
    • Jensen, S.T.1    Chen, G.2    Stoeckert, C.J.3
  • 26
    • 78649419087 scopus 로고    scopus 로고
    • Bayesian variable selection in structured high-dimensional covariate spaces with applications in genomics
    • MR2752615
    • LI, F. and ZHANG, N. R. (2010). Bayesian variable selection in structured high-dimensional covariate spaces with applications in genomics. J. Amer. Statist. Assoc. 105 1202-1214. MR2752615
    • (2010) J. Amer. Statist. Assoc , vol.105 , pp. 1202-1214
    • Li, F.1    Zhang, N.R.2
  • 27
    • 0345040873 scopus 로고    scopus 로고
    • Classification and regression by random forest
    • LIAW, A. and WIENER, M. (2002). Classification and regression by random forest. R news 2 18-22
    • (2002) R news , vol.2 , pp. 18-22
    • Liaw, A.1    Wiener, M.2
  • 29
    • 0000130839 scopus 로고
    • Bayesian variable selection in linear regression
    • MR0997578
    • MITCHELL, T. J. and BEAUCHAMP, J. J. (1988). Bayesian variable selection in linear regression. J. Amer. Statist. Assoc. 83 1023-1036. MR0997578
    • (1988) J. Amer. Statist. Assoc , vol.83 , pp. 1023-1036
    • Mitchell, T.J.1    Beauchamp, J.J.2
  • 30
    • 49549105778 scopus 로고    scopus 로고
    • The Bayesian lasso
    • MR2524001
    • PARK, T. and CASELLA, G. (2008). The Bayesian lasso. J. Amer. Statist. Assoc. 103 681-686. MR2524001
    • (2008) J. Amer. Statist. Assoc , vol.103 , pp. 681-686
    • Park, T.1    Casella, G.2
  • 31
    • 84987864133 scopus 로고    scopus 로고
    • EMVS: The EM approach to Bayesian variable selection
    • ROCKOVA, V. and GEORGE, E. I. (2014). EMVS: The EM approach to Bayesian variable selection. J. Amer. Statist. Assoc. 109 828-846.
    • (2014) J. Amer. Statist. Assoc , vol.109 , pp. 828-846
    • Rockova, V.1    George, E.I.2
  • 32
    • 79951528449 scopus 로고    scopus 로고
    • Variable selection for discriminant analysis with Markov random field priors for the analysis of microarray data
    • STINGO, F. and VANNUCCI, M. (2011). Variable selection for discriminant analysis with Markov random field priors for the analysis of microarray data. Bioinformatics 27 495-501
    • (2011) Bioinformatics , vol.27 , pp. 495-501
    • Stingo, F.1    Vannucci, M.2
  • 34
    • 0001287271 scopus 로고    scopus 로고
    • Regression shrinkage and selection via the lasso
    • MR1379242
    • TIBSHIRANI, R. (1996). Regression shrinkage and selection via the lasso. J. Roy. Statist. Soc. Ser. B 58267-288. MR1379242
    • (1996) J. Roy. Statist. Soc. Ser. B , pp. 58267-58288
    • Tibshirani, R.1
  • 36
    • 16244401458 scopus 로고    scopus 로고
    • Regularization and variable selection via the elastic net
    • MR2137327
    • ZOU, H. and HASTIE, T. (2005). Regularization and variable selection via the elastic net. J. R. Stat. Soc. Ser. B Stat. Methodol. 67 301-320. MR2137327
    • (2005) J. R. Stat. Soc. Ser. B Stat. Methodol , vol.67 , pp. 301-320
    • Zou, H.1    Hastie, T.2


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