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72 S.X. Lee et al. ter is based on mixtures of skew t-distributions. The effectiveness of JCM was illustrated on a germinal centre lymphoma dataset, in which JCM compares favourably with other available model-based methods and non-model-based methods. References 1. Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. J R Stat Soc, Ser B 39:1–38 2. McLachlan GJ, Peel D (2000) Finite mixture models. Wiley Series in Probability and Statistics, New York 3. McLachlan GJ, Do KA, Ambroise C (2004) Analyzing microarray gene expression data. Hoboken, New Jersey 4. Pyne S, Lee SX, Wang K, Irish J, Tamayo P, Nazaire MD, Duong T, Ng SK, Hafler D, Levy R, Nolan GP, Mesirov J, McLachlan GJ (2014) Joint modeling and registration of cell populations in cohorts of high-dimensional flow cytometric data. PLOS ONE 9(7):e100334 5. Pyne S, Hu X, Wang K, Rossin E, Lin TI, Maier LM, Baecher-Allan C, McLachlan GJ, Tamayo P, Hafler DA, De Jager PL, Mesirow JP (2009) Automated high-dimensional flow cytometric data analysis. Proc Natl Acad Sci USA 106:8519–8524 6. Li JQ, Barron AR (2000) Mixture density estimation. In: Solla SA, Leen TK, Mueller KR (eds) Advances in neural information processing systems. MIT Press, Cambridge, pp 279–285 7. McLachlan GJ, Krishnan T (2008) The EM algorithm and extensions, 2nd edn. Wiley- Interscience, Hokoben 8. McLachlan GJ, Peel D (1998) Robust cluster analysis via mixtures of multivariate t- distributions. In: Amin A, Dori D, Pudil P, Freeman H (eds) Lecture notes in computer science, vol 1451. Springer, Berlin, pp 658–666 9. Schwarz G (1978) Estimating the dimension of a model. Ann Stat 6:461–464 10. McLachlan GJ (1987) On bootstrapping the likelihood ratio test statistic for the number of components in a normal mixture. J R Stat Soc Ser C (Appl Stat) 36:318–324 11. McLachlan GJ, Peel D (1998) Robust cluster analysis via mixtures of multivariate t- distributions. In: Amin A, Dori D, Pudil P, Freeman H (eds) Lecture notes in computer science. Springer, Berlin, pp 658–666 12. Baek J, McLachlan GJ (2008) Mixtures of factor analyzers with common factor loadings for the clustering and visualisation of high-dimensional data. Technical Report NI08018-SCH, Preprint Series of the Isaac Newton Institute for Mathematical Sciences, Cambridge 13. Baek J, McLachlan GJ (2011) Mixtures of common t-factor analyzers for clustering highdimensional microarray data. Bioinformatics 27:1269–1276 14. McLachlan GJ, Bean RW, Peel D (2002) A mixture model-based approach to the clustering of microarray expression data. Bioinformatics 18:413–422 15. Yb Chan (2010) Hall P. Using evidence of mixed populations to select variables for clustering very high dimensional data. J Am Stat Assoc 105:798–809 16. Lee DD, Seung HS (1999) Learning the parts of objects by non-negative matrix factorization. Nature 401:788–791 17. Donoho D, Stodden V (2004) When does non-negative matrix factorization give correct decomposition into parts? In: Advances in neural information processing systems, vol 16. MIT Press, Cambridge, MA, pp 1141–1148 18. Golub GH, van Loan CF (1983) Matrix computation. The John Hopkins University Press, Baltimore 19. Kossenkov AV, Ochs MF (2009) Matrix factorization for recovery of biological processes from microarray data. Methods Enzymol 267:59–77 20. Johnstone IM, Lu AY (2009) On consistency and sparsity for principal components analysis in high dimensions. J Am Stat Assoc 104:682–693
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Saumyadipta Pyne · B.L.S. Prakasa
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Saumyadipta Pyne ⋅ B.L.S. Prakasa
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Foreword Big data is transforming t
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viii Preface We thank all the autho
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x Contents Managing Large-Scale Sta
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xii About the Editors biological, n
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2 S. Pyne et al. ethics. If data po
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4 S. Pyne et al. Therefore, as data
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