Bayesian regularization of Gaussian graphical models with measurement error
Tài liệu tham khảo
Cai, 2011, A constrained l1 minimization approach to sparse precision matrix estimation, J. Amer. Statist. Assoc., 106, 594, 10.1198/jasa.2011.tm10155
Carroll, 2006
Dempster, 1972, Covariance selection, Biometrics, 157, 10.2307/2528966
Deshpande, 2017
Friedman, 2008, Sparse inverse covariance estimation with the graphical lasso, Biostatistics, 9, 432, 10.1093/biostatistics/kxm045
Gan, 2018, Bayesian regularization for graphical models with unequal shrinkage, J. Amer. Statist. Assoc., 1
Huang, 2009, Predicting relapse in favorable histology wilms tumor using gene expression analysis: a report from the renal tumor committee of the children’s oncology group, Clin. Cancer Res., 15, 1770, 10.1158/1078-0432.CCR-08-1030
Johnstone, 2001, On the distribution of the largest eigenvalue in principal components analysis, Ann. Statist., 29, 295, 10.1214/aos/1009210544
Khare, 2015, A convex pseudolikelihood framework for high dimensional partial correlation estimation with convergence guarantees, J. R. Stat. Soc. Ser. B Stat. Methodol., 77, 803, 10.1111/rssb.12088
Krämer, 2009, Regularized estimation of large-scale gene association networks using graphical Gaussian models, BMC Bioinformatics, 10, 384, 10.1186/1471-2105-10-384
Lauritzen, 1996
Liang, 2018, An imputation–regularized optimization algorithm for high dimensional missing data problems and beyond, J. R. Stat. Soc. Ser. B Stat. Methodol., 80, 899, 10.1111/rssb.12279
Liu, 2017, Tiger: A tuning-insensitive approach for optimally estimating gaussian graphical models, Electron. J. Stat., 11, 241, 10.1214/16-EJS1195
Nghiem, 2018
O’Leary, 2015, Reference sequence (refseq) database at NCBI: current status, taxonomic expansion, and functional annotation, Nucleic Acids Res., 44, D733, 10.1093/nar/gkv1189
Petersen, 2008, The matrix cookbook, Tech. Univ. Denmark, 7, 510
Rocke, 2001, A model for measurement error for gene expression arrays, J. Comput. Biol., 8, 557, 10.1089/106652701753307485
Ročková, 2018, The spike-and-slab lasso, J. Amer. Statist. Assoc., 113, 431, 10.1080/01621459.2016.1260469
Ročková, 2018, Bayesian estimation of sparse signals with a continuous spike-and-slab prior, Ann. Statist., 46, 401, 10.1214/17-AOS1554
Segal, 2003, Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data, Nat. Genet., 34, 166, 10.1038/ng1165
Sørensen, 2015, Measurement error in lasso: Impact and likelihood bias correction, Statist. Sinica, 809
Tan, 2016, Replicates in high dimensions, with applications to latent variable graphical models, Biometrika, 103, 761, 10.1093/biomet/asw050
Turro, 2007, BGX: a Bioconductor package for the bayesian integrated analysis of affymetrix genechips, BMC Bioinformatics, 8, 439, 10.1186/1471-2105-8-439
Yuan, 2007, Model selection and estimation in the Gaussian graphical model, Biometrika, 94, 19, 10.1093/biomet/asm018
Zakharkin, 2005, Sources of variation in affymetrix microarray experiments, BMC Bioinform., 6, 214, 10.1186/1471-2105-6-214
Zhao, 2012, The huge package for high-dimensional undirected graph estimation in R, J. Mach. Learn. Res., 13, 1059
Honorio, 2012