Dimension Reduction for Model-based Clustering via Mixtures of ...

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Li, K. C. (1991). Sliced inverse regression for dimension reduction (with discussion).Journal of the American Statistical Association 86, 316–342.Li, K. C. (2000). High dimensional data analysis via the SIR/PHD approach. Unpublishedmanuscript.Lindsay, B. G. (1995). Mixture models: Theory, geometry and applications. In NSF-CBMS Regional Conference Series in Probability and Statistics, Volume 5. California:Institute of Mathematical Statistics: Hayward.Loader, C. (2012). locfit: Local Regression, Likelihood and Density Estimation. Rpackage version 1.5-8.McLachlan, G. J. and K. E. Basford (1988). Mixture Models: Inference and applicationsto clustering. New York: Marcel Dekker Inc.McLachlan, G. J., R. W. Bean, and L.-T. Jones (2007). Extension of the mixture offactor analyzers model to incorporate the multivariate t-distribution. ComputationalStatistics & Data Analysis 51 (11), 5327–5338.McLachlan, G. J. and D. Peel (1998). Robust cluster analysis via mixtures of multivariatet-distributions. In Lecture Notes in Computer Science, Volume 1451, pp. 658–666.Berlin: Springer-Verlag.McLachlan, G. J. and D. Peel (2000). Finite Mixture Models. New York: John Wiley &Sons.McNicholas, P. D. (2011). On model-based clustering, classification, and discriminantanalysis. Journal of the Iranian Statistical Society 10 (2), 181–199.McNicholas, P. D., K. R. Jampani, A. F. McDaid, T. B. Murphy, and L. Banks (2011).pgmm: Parsimonious Gaussian Mixture Models. R package version 1.0.McNicholas, P. D. and T. B. Murphy (2008). Parsimonious Gaussian mixture models.Statistics and Computing 18, 285–296.McNicholas, P. D., T. B. Murphy, A. F. McDaid, and D. Frost (2010). Serial andparallel implementations of model-based clustering via parsimonious Gaussian mixturemodels. Computational Statistics & Data Analysis 54 (3), 711–723.44
Meng, X. L. and D. B. Rubin (1993). Maximum likelihood estimation via the ECMalgorithm: a general framework. Biometrika 80, 267–278.Peel, D. and G. J. McLachlan (2000). Robust mixture modelling using the t-distribution.Statistics & Computing 10, 339–348.R Development Core Team (2012). R: A Language and Environment for StatisticalComputing. Vienna, Austria: R Foundation for Statistical Computing.Raftery, A. E. and N. Dean (2006). Variable selection for model-based clustering. Journalof the American Statistical Association 101 (473), 168–178.Rand, W. M. (1971). Objective criteria for the evaluation of clustering methods. Journalof the American Statistical Association 66, 846–850.Reaven, G. M. and R. G. Miller (1979). An attempt to define the nature of chemicaldiabetes using a multidimensional analysis. Diabetologia 16, 17–24.Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics 6, 461–464.Scrucca, L. (2010). Dimension reduction for model-based clustering. Statistics & Computing20 (4), 471–484.Streuli, H. (1973). Der heutige stand der kaffeechemie. In Association ScientifiqueInternational du Cafe, 6th International Colloquium on Coffee Chemistry, Bogatá,Columbia, pp. 61–72.Venables, W. N. and B. D. Ripley (2002). Modern Applied Statistics with S (Fourthed.). New York: Springer.45
Page 1: Dimension Reduction for Model-based
Page 5 and 6: List of Tables2.1 Nomenclature for
Page 7: Chapter 1IntroductionClustering alg
Page 10 and 11: 2.1 Gaussian Mixture ModelsFrom McL
Page 12 and 13: • λ g is a scalar which specifie
Page 14 and 15: In the M-step of the algorithm, the
Page 16 and 17: simple example. McLachlan and Peel
Page 18: For the tEIGEN models, parameter es
Page 21 and 22: onto the reduced subspace and their
Page 23 and 24: to the others. Specifically, we nee
Page 25 and 26: Definition 3.2 The tMMDR variables,
Page 27 and 28: • the forward step evaluates the
Page 29 and 30: Chapter 4ApplicationsWe now focus o
Page 31 and 32: Figure 4.2: Pairs plot for the thre
Page 33 and 34: Table 4.1: Average ARI (with standa
Page 35 and 36: The known classification of the cof
Page 37 and 38: Table 4.5: Variable description for
Page 39 and 40: 4.2.3 Australian Crabs DataCampbell
Page 41 and 42: Figure 4.6: Plots of tMMDR directio
Page 43 and 44: those notes. The results may indica
Page 45 and 46: Table 4.16: A classification table
Page 47 and 48: Chapter 5ConclusionThis thesis intr
Page 49: Forina, M., C. Armanino, M. Castino

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