메뉴 건너뛰기




Volumn 8, Issue , 2007, Pages 1145-1164

Penalized model-based clustering with application to variable selection

Author keywords

BIC; EM; Mixture model; Penalized likelihood; Shrinkage; Soft thresholding

Indexed keywords

ALGORITHMS; MATHEMATICAL MODELS; MAXIMUM LIKELIHOOD; PARAMETER ESTIMATION; PROBLEM SOLVING; SAMPLING;

EID: 34249029861     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article
Times cited : (241)

References (50)
  • 1
    • 8844241460 scopus 로고    scopus 로고
    • Class discovery and classification of tumor samples using mixture modeling of gene expression data
    • R. Alexandridis, S. Lin, and M. Irwin. Class discovery and classification of tumor samples using mixture modeling of gene expression data. Bioinformatics, 20:2546-2552, 2004.
    • (2004) Bioinformatics , vol.20 , pp. 2546-2552
    • Alexandridis, R.1    Lin, S.2    Irwin, M.3
  • 2
    • 33745156863 scopus 로고    scopus 로고
    • Some theory for Fisher's linear discriminant function, "naive Bayes", and some alternatives when there are many more variables than observations
    • P. J. Bickel, and E. Levina. Some theory for Fisher's linear discriminant function, "naive Bayes", and some alternatives when there are many more variables than observations. Bernoulli, 10:989-1010, 2004.
    • (2004) Bernoulli , vol.10 , pp. 989-1010
    • Bickel, P.J.1    Levina, E.2
  • 3
    • 0035478854 scopus 로고    scopus 로고
    • Random forests
    • L. Breiman. Random forests. Machine Learning 45:5-32, 2001.
    • (2001) Machine Learning , vol.45 , pp. 5-32
    • Breiman, L.1
  • 5
    • 0020998698 scopus 로고
    • On using principal components before separating a mixture of two multivariate normal distributions
    • W. C. Chang. On using principal components before separating a mixture of two multivariate normal distributions. Applied Statistics, 32:267-275, 1983.
    • (1983) Applied Statistics , vol.32 , pp. 267-275
    • Chang, W.C.1
  • 7
    • 0002629270 scopus 로고
    • Maximum likelihood from incomplete data via the EM algorithm (with discussion)
    • A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood from incomplete data via the EM algorithm (with discussion). JRSS-B, 39:1-38, 1977.
    • (1977) JRSS-B , vol.39 , pp. 1-38
    • Dempster, A.P.1    Laird, N.M.2    Rubin, D.B.3
  • 8
    • 4944239996 scopus 로고    scopus 로고
    • The estimation of prediction error: Covariance penalties and cross-validation
    • B. Efron. The estimation of prediction error: covariance penalties and cross-validation. JASA, 99:619-632, 2004.
    • (2004) JASA , vol.99 , pp. 619-632
    • Efron, B.1
  • 9
    • 85169997994 scopus 로고    scopus 로고
    • B. Efron, T. Hastie T, I. Johnstone I, and R. Tibshirani. Least angle regression. Annals of Statistics, 32:407-499, 2004.
    • B. Efron, T. Hastie T, I. Johnstone I, and R. Tibshirani. Least angle regression. Annals of Statistics, 32:407-499, 2004.
  • 10
    • 0032441150 scopus 로고    scopus 로고
    • Cluster analysis and display of genome-wide expression patterns
    • M. Eisen, P. Spellman, P. Brown, and D. Botstein. Cluster analysis and display of genome-wide expression patterns. PNAS, 95:14863-14868, 1998.
    • (1998) PNAS , vol.95 , pp. 14863-14868
    • Eisen, M.1    Spellman, P.2    Brown, P.3    Botstein, D.4
  • 11
    • 1542784498 scopus 로고    scopus 로고
    • Variable selection via nonconcave penalized likelihood and its Oracle properties
    • J. Fan, and R. Li. Variable selection via nonconcave penalized likelihood and its Oracle properties. JASA, 96:1348-1360, 2001.
    • (2001) JASA , vol.96 , pp. 1348-1360
    • Fan, J.1    Li, R.2
  • 12
    • 0032269108 scopus 로고    scopus 로고
    • How many clusters? Which clustering methods? - Answers via model-based cluster analysis
    • C. Fraley, and A. E. Raftery. How many clusters? Which clustering methods? - Answers via model-based cluster analysis. The Computer Journal, 41:578-588, 1998.
    • (1998) The Computer Journal , vol.41 , pp. 578-588
    • Fraley, C.1    Raftery, A.E.2
  • 14
    • 33645279316 scopus 로고    scopus 로고
    • Bayesian regularization for normal mixture estimation and model-based clustering
    • Technical report 486, Dept. of Statistics, University of Washington
    • C. Fraley, and A. E. Raftery. Bayesian regularization for normal mixture estimation and model-based clustering. Technical report 486, Dept. of Statistics, University of Washington, 2005.
    • (2005)
    • Fraley, C.1    Raftery, A.E.2
  • 15
    • 8644255832 scopus 로고    scopus 로고
    • Clustering objects on subsets of attributes (with discussion)
    • J. H. Friedman, and J. J. Meulman. Clustering objects on subsets of attributes (with discussion). J. R. Stat. Soc. Ser. B, 66:815-849, 2004.
    • (2004) J. R. Stat. Soc. Ser. B , vol.66 , pp. 815-849
    • Friedman, J.H.1    Meulman, J.J.2
  • 16
    • 85142170070 scopus 로고    scopus 로고
    • D. Ghosh D, and A. M. Chinnaiyan. (2002). Mixture modeling of gene expression data from microarray experiments. Bioinformatics, 18:275-286, 2002.
    • D. Ghosh D, and A. M. Chinnaiyan. (2002). Mixture modeling of gene expression data from microarray experiments. Bioinformatics, 18:275-286, 2002.
  • 18
    • 0000357775 scopus 로고
    • On use of the EM for penalized likelihood estimation
    • P. J. Green. On use of the EM for penalized likelihood estimation. J. R. Stat. Soc. Ser. B, 52:443-452, 1990.
    • (1990) J. R. Stat. Soc. Ser. B , vol.52 , pp. 443-452
    • Green, P.J.1
  • 20
    • 33646705989 scopus 로고    scopus 로고
    • Discussion of 'Clustering objects on subsets of attributes' by Friedman and Meulman
    • P. D. Hoff. Discussion of 'Clustering objects on subsets of attributes' by Friedman and Meulman. J. R. Stat. Soc. Ser. B, 66:845-846, 2004.
    • (2004) J. R. Stat. Soc. Ser. B , vol.66 , pp. 845-846
    • Hoff, P.D.1
  • 21
    • 33644863012 scopus 로고    scopus 로고
    • Subset clustering of binary sequences, with an application to genomic abnormality data
    • P. D. Hoff. Subset clustering of binary sequences, with an application to genomic abnormality data. Biometrics, 61:1027-1036, 2005.
    • (2005) Biometrics , vol.61 , pp. 1027-1036
    • Hoff, P.D.1
  • 22
    • 33645992615 scopus 로고    scopus 로고
    • Model-based subspace clustering
    • P. D. Hoff. Model-based subspace clustering. Bayesian Analysis, 1:321-344, 2006.
    • (2006) Bayesian Analysis , vol.1 , pp. 321-344
    • Hoff, P.D.1
  • 24
    • 22544479764 scopus 로고    scopus 로고
    • Markov chain Monte Carlo methods and the label switching problem in Bayesian mixture modeling
    • A. Jasra, C. C. Holmes, and D. A. Stephens. Markov chain Monte Carlo methods and the label switching problem in Bayesian mixture modeling. Statistical Science, 20:50-67, 2005.
    • (2005) Statistical Science , vol.20 , pp. 50-67
    • Jasra, A.1    Holmes, C.C.2    Stephens, D.A.3
  • 25
    • 33845734547 scopus 로고    scopus 로고
    • Variable selection in clustering via Dirichlet process mixture models
    • S. Kim, M. G. Tadesse, and M. Vannucci. Variable selection in clustering via Dirichlet process mixture models. Biometrika, 93:877-893, 2006.
    • (2006) Biometrika , vol.93 , pp. 877-893
    • Kim, S.1    Tadesse, M.G.2    Vannucci, M.3
  • 26
    • 18144444239 scopus 로고    scopus 로고
    • Cluster-Rasch models for microarray gene expression data
    • 2: research0031.1-0031.13
    • H. Li, and F. Hong. Cluster-Rasch models for microarray gene expression data. Genome Biology, 2: research0031.1-0031.13, 2001.
    • (2001) Genome Biology
    • Li, H.1    Hong, F.2
  • 27
    • 15044339834 scopus 로고    scopus 로고
    • Bayesian clustering with variable and transformation selection (with discussion)
    • J. S. Liu, J. L. Zhang, M. J. Palumbo, C. E. Lawrence. Bayesian clustering with variable and transformation selection (with discussion). Bayesian Statistics, 7:249-275, 2003.
    • (2003) Bayesian Statistics , vol.7 , pp. 249-275
    • Liu, J.S.1    Zhang, J.L.2    Palumbo, M.J.3    Lawrence, C.E.4
  • 29
    • 0036203115 scopus 로고    scopus 로고
    • A mixture model-based approach to the clustering of microarray expression data
    • G. J. McLachlan, R. W. Bean, and D. Peel. A mixture model-based approach to the clustering of microarray expression data. Bioinformatics, 18:413-422, 2002.
    • (2002) Bioinformatics , vol.18 , pp. 413-422
    • McLachlan, G.J.1    Bean, R.W.2    Peel, D.3
  • 33
    • 0035999977 scopus 로고    scopus 로고
    • A comparative review of statistical methods for discovering differentially expressed genes in replicated microarray experiments
    • W. Pan. A comparative review of statistical methods for discovering differentially expressed genes in replicated microarray experiments. Bioinformatics, 12:546-554, 2002.
    • (2002) Bioinformatics , vol.12 , pp. 546-554
    • Pan, W.1
  • 34
    • 33750022956 scopus 로고    scopus 로고
    • Semi-supervised learning via penalized mixture model with application to microarray sample classification
    • W. Pan, X. Shen, A. Jiang, and R. P. Hebbel. Semi-supervised learning via penalized mixture model with application to microarray sample classification. Bioinformatics, 22:2388-2395, 2006.
    • (2006) Bioinformatics , vol.22 , pp. 2388-2395
    • Pan, W.1    Shen, X.2    Jiang, A.3    Hebbel, R.P.4
  • 35
    • 34249096870 scopus 로고    scopus 로고
    • A. E. Raftery. Discussion of Bayesian clustering with variable and transformation selection by Liu et al. Bayesian Statistics, 7:266-271, 2003.
    • A. E. Raftery. Discussion of "Bayesian clustering with variable and transformation selection" by Liu et al. Bayesian Statistics, 7:266-271, 2003.
  • 37
    • 18244378520 scopus 로고    scopus 로고
    • On Bayesian analysis of mixtures with an unknown number of components
    • S. Richardson, and P. J. Green. On Bayesian analysis of mixtures with an unknown number of components. JRSS-B, 59:731-758, 1997.
    • (1997) JRSS-B , vol.59 , pp. 731-758
    • Richardson, S.1    Green, P.J.2
  • 38
    • 0000120766 scopus 로고
    • Estimating the dimensions of a model
    • G. Schwarz. Estimating the dimensions of a model. Annals of Statistics, 6:461-464, 1978.
    • (1978) Annals of Statistics , vol.6 , pp. 461-464
    • Schwarz, G.1
  • 41
    • 0034911875 scopus 로고    scopus 로고
    • An efficient and robust statistical modeling approach to discover differentially expressed genes using genomic expression profiles
    • J. G. Thomas, J. M. Olson, S. J. Tapscott, and L. P. Zhao. An efficient and robust statistical modeling approach to discover differentially expressed genes using genomic expression profiles. Genome Research, 11:1227-1236, 2001.
    • (2001) Genome Research , vol.11 , pp. 1227-1236
    • Thomas, J.G.1    Olson, J.M.2    Tapscott, S.J.3    Zhao, L.P.4
  • 42
    • 0001287271 scopus 로고    scopus 로고
    • Regression shrinkage and selection via the Lasso
    • R. Tibshirani. Regression shrinkage and selection via the Lasso. JRSS-B, 58:267-288, 1996.
    • (1996) JRSS-B , vol.58 , pp. 267-288
    • Tibshirani, R.1
  • 43
    • 2342533421 scopus 로고    scopus 로고
    • Class prediction by nearest shrunken centroids, with application to DNA microarrays
    • R. Tibshirani, T. Hastie, B. Narasimhan, and G. Chu. Class prediction by nearest shrunken centroids, with application to DNA microarrays. Statistical Science, 18:104-117, 2003.
    • (2003) Statistical Science , vol.18 , pp. 104-117
    • Tibshirani, R.1    Hastie, T.2    Narasimhan, B.3    Chu, G.4
  • 45
    • 0036649020 scopus 로고    scopus 로고
    • Large-scale prediction of saccharomyces cerevisiae gene function using overlapping transcriptional clusters
    • L. F. Wu, T. R. Hughes, A. P. Davierwala, M. D. Robinson, R. Stoughton, and S. J. Altschuler. Large-scale prediction of saccharomyces cerevisiae gene function using overlapping transcriptional clusters. Nature Genetics, 31:255-265, 2002.
    • (2002) Nature Genetics , vol.31 , pp. 255-265
    • Wu, L.F.1    Hughes, T.R.2    Davierwala, A.P.3    Robinson, M.D.4    Stoughton, R.5    Altschuler, S.J.6
  • 46
    • 31044452892 scopus 로고    scopus 로고
    • Gene function prediction by a combined analysis of gene expression data and protein-protein interaction data
    • G. Xiao, and W. Pan. Gene function prediction by a combined analysis of gene expression data and protein-protein interaction data. Journal of Bioinformatics and Computational Biology, 3:1371-1389, 2005.
    • (2005) Journal of Bioinformatics and Computational Biology , vol.3 , pp. 1371-1389
    • Xiao, G.1    Pan, W.2
  • 47
    • 0034800371 scopus 로고    scopus 로고
    • Principal component analysis for clustering gene expression data
    • K. Y. Yeung, and W. L. Ruzzo. Principal component analysis for clustering gene expression data. Bioinformatics, 17:763-774, 2001.
    • (2001) Bioinformatics , vol.17 , pp. 763-774
    • Yeung, K.Y.1    Ruzzo, W.L.2
  • 48
    • 0034782618 scopus 로고    scopus 로고
    • Model-based clustering and data transformations for gene expression data
    • K. Y. Yeung, C. Fraley, A. Murua, A. E. Raftery, and W. L. Ruzzo. Model-based clustering and data transformations for gene expression data. Bioinformatics, 17:977-987, 2001.
    • (2001) Bioinformatics , vol.17 , pp. 977-987
    • Yeung, K.Y.1    Fraley, C.2    Murua, A.3    Raftery, A.E.4    Ruzzo, W.L.5
  • 49
    • 0036790999 scopus 로고    scopus 로고
    • Transitive functional annotation by shortest-path analysis of gene expression data
    • X. Zhou, M. C. Kao, and W. H. Wong. Transitive functional annotation by shortest-path analysis of gene expression data. Proc Natl Acad Sci USA, 99:12783-12788, 2002.
    • (2002) Proc Natl Acad Sci USA , vol.99 , pp. 12783-12788
    • Zhou, X.1    Kao, M.C.2    Wong, W.H.3
  • 50
    • 33645581305 scopus 로고    scopus 로고
    • On the "Degrees of Freedom" of the Lasso
    • Technical report, Dept. of Statistics, Stanford University, Available at
    • H. Zou, T. Hastie, and R. Tibshirani. On the "Degrees of Freedom" of the Lasso. Technical report, Dept. of Statistics, Stanford University, 2004. Available at http://stat.stanford.edu/~hastie/pub.htm.
    • (2004)
    • Zou, H.1    Hastie, T.2    Tibshirani, R.3


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.