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Items: 1 to 20 of 74

1.

Testing for group structure in high-dimensional data.

McLachlan GJ, Rathnayake SI.

J Biopharm Stat. 2011 Nov;21(6):1113-25. doi: 10.1080/10543406.2011.608342.

PMID:
22023680
2.

Mixtures of common t-factor analyzers for clustering high-dimensional microarray data.

Baek J, McLachlan GJ.

Bioinformatics. 2011 May 1;27(9):1269-76. doi: 10.1093/bioinformatics/btr112. Epub 2011 Mar 3.

PMID:
21372081
3.
4.

Evaluating mixture modeling for clustering: recommendations and cautions.

Steinley D, Brusco MJ.

Psychol Methods. 2011 Mar;16(1):63-79. doi: 10.1037/a0022673.

PMID:
21319900
5.

Classification of microarray data with factor mixture models.

Martella F.

Bioinformatics. 2006 Jan 15;22(2):202-8. Epub 2005 Nov 15.

PMID:
16287938
6.

A mixture model-based approach to the clustering of microarray expression data.

McLachlan GJ, Bean RW, Peel D.

Bioinformatics. 2002 Mar;18(3):413-22.

PMID:
11934740
7.

Model-based clustering and data transformations for gene expression data.

Yeung KY, Fraley C, Murua A, Raftery AE, Ruzzo WL.

Bioinformatics. 2001 Oct;17(10):977-87.

PMID:
11673243
8.

Mixture models for eye-tracking data: a case study.

Pauler DK, Escobar MD, Sweeney JA, Greenhouse J.

Stat Med. 1996 Jul 15;15(13):1365-76.

PMID:
8841647
9.

Simultaneous gene clustering and subset selection for sample classification via MDL.

J├Ârnsten R, Yu B.

Bioinformatics. 2003 Jun 12;19(9):1100-9.

PMID:
12801870
10.

Bayesian mixture model based clustering of replicated microarray data.

Medvedovic M, Yeung KY, Bumgarner RE.

Bioinformatics. 2004 May 22;20(8):1222-32. Epub 2004 Feb 10.

PMID:
14871871
11.

Mixture modelling for cluster analysis.

McLachlan GJ, Chang SU.

Stat Methods Med Res. 2004 Oct;13(5):347-61.

PMID:
15516030
12.

Clustering of high-dimensional gene expression data with feature filtering methods and diffusion maps.

Xu R, Damelin S, Nadler B, Wunsch DC 2nd.

Artif Intell Med. 2010 Feb-Mar;48(2-3):91-8. doi: 10.1016/j.artmed.2009.06.001. Epub 2009 Dec 4.

PMID:
19962867
13.

Internal validation of risk models in clustered data: a comparison of bootstrap schemes.

Bouwmeester W, Moons KG, Kappen TH, van Klei WA, Twisk JW, Eijkemans MJ, Vergouwe Y.

Am J Epidemiol. 2013 Jun 1;177(11):1209-17. doi: 10.1093/aje/kws396. Epub 2013 May 9.

PMID:
23660796
14.

Bootstrapping with models for count data.

Manly BF.

J Biopharm Stat. 2011 Nov;21(6):1164-76. doi: 10.1080/10543406.2011.607748.

PMID:
22023684
15.

Adapting prediction error estimates for biased complexity selection in high-dimensional bootstrap samples.

Binder H, Schumacher M.

Stat Appl Genet Mol Biol. 2008;7(1):Article12. doi: 10.2202/1544-6115.1346. Epub 2008 Mar 14.

PMID:
18384265
16.

CHull as an alternative to AIC and BIC in the context of mixtures of factor analyzers.

Bulteel K, Wilderjans TF, Tuerlinckx F, Ceulemans E.

Behav Res Methods. 2013 Sep;45(3):782-91. doi: 10.3758/s13428-012-0293-y.

PMID:
23307573
17.

New resampling method for evaluating stability of clusters.

Gana Dresen IM, Boes T, Huesing J, Neuhaeuser M, Joeckel KH.

BMC Bioinformatics. 2008 Jan 24;9:42. doi: 10.1186/1471-2105-9-42.

18.

K-means may perform as well as mixture model clustering but may also be much worse: comment on Steinley and Brusco (2011).

Vermunt JK.

Psychol Methods. 2011 Mar;16(1):82-8; discussion 89-92. doi: 10.1037/a0020144.

PMID:
21381819
19.
20.

Simultaneous feature selection and clustering using mixture models.

Law MH, Figueiredo MA, Jain AK.

IEEE Trans Pattern Anal Mach Intell. 2004 Sep;26(9):1154-66.

PMID:
15742891

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