Finite Mixture ModelsJohn Wiley & Sons, 2. 10. 2000 - Počet stran: 464 An up-to-date, comprehensive account of major issues in finite mixture modeling This volume provides an up-to-date account of the theory and applications of modeling via finite mixture distributions. With an emphasis on the applications of mixture models in both mainstream analysis and other areas such as unsupervised pattern recognition, speech recognition, and medical imaging, the book describes the formulations of the finite mixture approach, details its methodology, discusses aspects of its implementation, and illustrates its application in many common statistical contexts. Major issues discussed in this book include identifiability problems, actual fitting of finite mixtures through use of the EM algorithm, properties of the maximum likelihood estimators so obtained, assessment of the number of components to be used in the mixture, and the applicability of asymptotic theory in providing a basis for the solutions to some of these problems. The author also considers how the EM algorithm can be scaled to handle the fitting of mixture models to very large databases, as in data mining applications. This comprehensive, practical guide: * Provides more than 800 references-40% published since 1995 * Includes an appendix listing available mixture software * Links statistical literature with machine learning and pattern recognition literature * Contains more than 100 helpful graphs, charts, and tables Finite Mixture Models is an important resource for both applied and theoretical statisticians as well as for researchers in the many areas in which finite mixture models can be used to analyze data. |
Obsah
General Introduction | 1 |
1 | 7 |
Modeling of Asymmetrical Data | 14 |
8 | 94 |
Multivariate Normal Mixtures | 116 |
Bayesian Approach to Mixture Analysis | 124 |
Mixtures with Nonnormal Components | 135 |
Assessing the Number of Components in Mixture Models | 175 |
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Aitkin American Statistical Association analysis applications approximation asymptotic Basford Bayesian binomial Biometrics bootstrap Celeux classification clustering component densities component membership component-covariance matrices computation considered convergence corresponding covariance matrix criterion data set denotes E-step EM algorithm example f(yj factor analyzers finite mixture models fitting a mixture given groups hidden Markov models homoscedastic IEM algorithm ith component joint sets Journal of Statistical latent likelihood function linear local maximizers LRTS Markov chain maximizer maximum likelihood McLachlan method mixing proportions mixture density mixture distributions mixtures of normal multivariate normal components normal distributions normal mixture model null distribution number of components observed data observed information matrix obtained overdispersion Peel Plot Poisson Poisson regression posterior probabilities prior probabilities of component problem Raftery random regression model reoperation Royal Statistical Society sample Section simulation solution specified univariate normal unknown parameters updated variables variance vector