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Original Articles

Testing for Group Structure in High-Dimensional Data

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Pages 1113-1125 | Received 05 Apr 2011, Accepted 23 May 2011, Published online: 24 Oct 2011

REFERENCES

  • Aitkin , M. , Anderson , D. , Hinde , J. ( 1981 ). Statistical modelling of data on teaching styles (with discussion) . Journal of the Royal Statistical Society/B 144 : 419 – 461 .
  • Ambroise , C. , McLachlan , G. J. ( 2002 ). Selection bias in gene extraction on basis of microarray gene expression data . Proceedings of the National Academy of Sciences USA 99 : 6562 – 6566 .
  • Baek , J. , McLachlan , G. J. ( 2011 ). Mixtures of common t-factor analyzers for clustering high-dimensional microarray data . Bioinformatics 27 : 1269 – 1276 .
  • Baek , J. , McLachlan , G. J. , Flack , L. ( 2010 ). Mixtures of factor analyzers with common factor loadings: applications to the clustering and visualisation of high-dimensional data . IEEE Transactions on Pattern Analysis and Machine Intelligence 32 : 1298 – 1309 .
  • Barnard , G. A. ( 1963 ). Contribution to the discussion of paper by M.S. Bartlett . Journal of the Royal Statistical Society/B 25 : 294 .
  • Dempster , A. P. , Laird , N. M. , Rubin , D. B. ( 1977 ). Maximum likelihood from incomplete data via the EM algorithm (with discussion) . Journal of the Royal Statistical Society: Series B 39 : 1 – 38 .
  • Dudoit , S. , Fridlyand , J. , Speed , T. P. ( 2002 ). Comparison of discrimination methods for the classification of tumors using gene expression data . Journal of the American Statistical Association 97 : 77 – 87 .
  • Efron , B. ( 1979 ). Bootstrap methods: Another look at the jackknife . Annals of Statistics 7 : 1 – 26 .
  • Efron , B. ( 1982 ). The Jackknife, the Bootstrap and Other Resampling Plans . Philadelphia : SIAM .
  • Efron , B. , Tibshirani , R. ( 1993 ). An Introduction to the Bootstrap . London : Chapman & Hall .
  • Fan , J. , Fan , Y. ( 2008 ). High dimensional classification using features annealed independence rules . Annals of Statistics 36 : 2605 – 2637 .
  • Golub , T. R. , Slonim , D. K. , Tamayo , P. , Huard , C. , Gassenbeck , M. , Mesirov , J. P. , Coller , H. , Loh , M. L. , Downing , J. R. , Caligiuri , M. A. , Bloomfield , C. D. , Lander E. S. (1999). Molecular classification of cancer: class discovery. Science 286:531–537.
  • Hall , P. , Pittelkow , Y. , Ghosh , M. ( 2008 ). Theoretic measures of relative performance of classifiers for high-dimensional data with small sample sizes . Journal of the Royal Statistical Society B 70 : 158 – 173 .
  • Hinton , G. E. , Dayan , P. , Revow , M. ( 1997 ). Modeling the manifolds of images of handwritten digits . IEEE Transactions on Neural Networks 68 : 65 – 73 .
  • Hoaglin , D. C. ( 1985 ). Using quantiles to study shape . In: Hoaglin , D. C. , Mosteller , F. , Tukey , J. W. , eds. Explaining Data Tables, Trends, and Shapes . New York : Wiley , pp. 417 – 460 .
  • Hope , A. C. ( 1968 ). A simplified Monte Carlo significant test procedure . Journal of the Royal Statistical Society: Series B 30 : 582 – 598 .
  • Hubert , L. , Arabie , P. ( 1985 ). Comparing partitions . Journal of Classification 2 : 193 – 218 .
  • Liu , Y. , Hayes , D. N. , Nobel , A. , Marron , J. S. ( 2010 ). Statistical significance of clustering for high-dimension, low-sample size data . Journal of the American Statistical Association 103 : 1281 – 1293 .
  • McLachlan , G. J. ( 1987 ). On bootstrapping the likelihood ratio test statistic for the number of components in a normal mixture . Applied Statistics 36 : 318 – 324 .
  • McLachlan , G. J. , Bean , R. W. , Peel , D. ( 2002 ). A mixture model-based approach to the clustering of microarray expression data . Bioinformatics 18 : 413 – 422 .
  • McLachlan , G. J. , Bean , R. W. , Ben-Tovim Jones , L. ( 2006 ). A simple implementation of a normal mixture approach to differential gene expression in multiclass microarrays . Bioinformatics 22 : 1608 – 1615 .
  • McLachlan , G. J. , Bean , R. W. , Ben-Tovim Jones , L. ( 2007 ). Extension of the mixture of factor analyzers model to incorporate the multivariate t distribution . Computational Statistics and Data Analysis 51 : 5327 – 5338 .
  • McLachlan , G. J. , Krishnan , T. ( 2008 ). The EM Algorithm and Extensions . New York : Wiley .
  • McLachlan , G. J. , Peel , D. ( 2000a ). Finite Mixture Models . New York : Wiley .
  • McLachlan . G. J. , Peel , D. ( 2000b ). Mixtures of factor analyzers . In: Langley , P. ed. Proceedings of the Seventeenth International Conference on Machine Learning , (Ed.). San Francisco : Morgan Kaufmann , pp. 599 – 606 .
  • McLachlan , G. J. , Peel , D. , Bean , R. W. ( 2003 ). Modelling high-dimensional data by mixtures of factor analyzers . Computational Statistics & Data Analysis 41 : 379 – 388 .
  • Pyne , S. , Hu , X. , Wang , K. , Rossin , E. , Lin , T.-I. , Maier , L.M. , Baecher-Allan , C. , McLachlan , G. J. , Tamayo , P. , Hafler , D. A. , De Jager , P. L. , Mesirov , J. P. ( 2009 ). Automated high-dimensional flow cytometric data analysis . Proceedings of the National Academy of Sciences USA 106 : 8519 – 8524 .
  • Schwarz , G. ( 1978 ). Estimating the dimension of a model . Annals of Statistics 6 : 461 – 464 .

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