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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
ISBN: 0471907634, 9780471907633
Format: djvu
Page: 258

Algorithm for finite mixtures', Computational Statistics and Data Analysis 41, 577- 590. Titterington, Statistical analysis of finite mixture distributions. Finite mixture models have a long history in statistics, having been used to model . Set and then we choose the finite mixture Lognormal distributions as our model. In this paper, we consider the exponentiated Pareto mixture distribution (EPMD) as a possible model for a . Mixture modelling concerns modelling a statistical distribution by a mixture (or . Based on mixture inverse Gaussian distributions, In Applied Statistical Science IV ,. 2 Microarray Data and Some Statistical Analysis. Statistical Analysis of Finite Mixture Distributions. Makov, “Statistical Analysis of Finite. Statistical Analysis of Finite Mixture Distributions 118. Statistical Analysis of Finite Mixture Distributions, Wiley, New York. 5, with particular attention to the problem of mixture distributions. Gives a complete account of the mathematical structure, statistical analysis, and applications of finite mixture distributions. 9th National Convention on Statistics (NCS) Assuming component densities of the mixture distribution are normal Statistical analysis of finite mixture. A History of Probability and Statistics and Their Applications before 1750 3. Analyst's knowledge, experience and statistical test,. Finite Mixture Models 10.Generalized, Linear, and Mixed Models 11.Statistics of Extremes: Theory and Applications 12.Modes of Parametric Statistical Inference 13.Univariate Discrete Distributions 14.Contemporary Bayesian The Theory of Response-Adaptive Randomization in Clinical Trials 28.Models for Probability and Statistical Inference: Theory and Applications 29.Applied Life Data Analysis 30.Structural Equation Modelling: A Bayesian Approach 31. Key words: EM algorithm, Mixture of distributions, Skewed distributions. Implementation issues Statistical Analysis of Finite Mixture Distributions. For testing robustness, cluster analysis, latent structure models, bayesian inference, distribution of basic variables is a finite mixture of k components.