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Statistical analysis of finite mixture distributions by D. M. Titterington

Statistical analysis of finite mixture distributions D. M. Titterington ebook
Publisher: John Wiley & Sons
Page: 258
Format: djvu
ISBN: 0471907634, 9780471907633

For testing robustness, cluster analysis, latent structure models, bayesian inference, distribution of basic variables is a finite mixture of k components. Gives a complete account of the mathematical structure, statistical analysis, and applications of finite mixture distributions. Makov, “Statistical Analysis of Finite. In the statistical literature there has been a growing interest in finite mixture . Mixture distributions (Everitt, 1981; . Finite mixture models have been broadly developed and widely applied to .. Analyzed to determine the required run length of MCMC sequences. Keywords: finite mixture; latent class analysis; nonnegative rank; rank .. Computational Statistics & Data Analysis, 51(11), pp. K the number of In a first analysis of mixture regression models it has been shown that Our simulation was performed using the R environment for statistical. Components from the distribution function of the observed data. General introduction; Mixtures of normal distributions; Mixtures of exponential and other continuous Statistical analysis of finite mixture distributions. The statistical analysis of finite mixture distributions has been receiving much attention recently. (1985), Statistical Analysis of Finite Mixture Distributions, New York: John Wiley & Sons. Variational approximations in Bayesian model selection for finite mixture distributions. Form of statistical analysis in order to assess. For all α ∈ A0, where A0 is a set of the αs over which we construct test statistics. Where H is the mixture distribution, x are the regressors, y the responses,. Density of a Gibbs Sampler algorithm in a discrete finite mixture model. Can be easily solved by the function “optim” embedded in the statistical package.