Mixture models : inference and applications to clustering

General Introduction Introduction History of Mixture Models Background to the General Classification Problem Mixture Likelihood Approach to Clustering Identifiability Likelihood Estimation for Mixture Models via EM Algorithm Start Values for EMm Algorithm Properties of Likelihood Estimators for Mixture Models Information Matrix for Mixture Models Tests for the Number of Components in a Mixture Partial Classification of the Data Classification Likelihood Approach to Clustering Mixture Models with Normal Components Likelihood Estimation for a Mixture of Normal Distribution Normal Homoscedastic Components Asymptotic Relative Efficiency of the Mixture Likelihood Approach Expected and Observed Information Matrices Assessment of Normality for Component Distributions: Partially Classified Data Assessment of Typicality: Partially Classified Data Assessment of Normality and Typicality: Unclassified Data Robust Estimation for Mixture Models Applications of Mixture Models to Two-Way Data Sets Introduction Clustering of Hemophilia Data Outliers in Darwin's Data Clustering of Rare Events Latent Classes of Teaching Styles Estimation of Mixing Proportions Introduction Likelihood Estimation Discriminant Analysis Estimator Asymptotic Relative Efficiency of Discriminant Analysis Estimator Moment Estimators Minimum Distance Estimators Case Study Homogeneity of Mixing Proportions Assessing the Performance of the Mixture Likelihood Approach to Clustering Introduction Estimators of the Allocation Rates Bias Correction of the Estimated Allocation Rates Estimated Allocation Rates of Hemophilia Data Estimated Allocation Rates for Simulated Data Other Methods of Bias Corrections Bias Correction for Estimated Posterior Probabilities Partitioning of Treatment Means in ANOVA Introduction Clustering of Treatment Means by the Mixture Likelihood Approach Fitting of a Normal Mixture Model to a RCBD with Random Block Effects Some Other Methods of Partitioning Treatment Means Example 1 Example 2 Example 3 Example 4 Mixture Likelihood Approach to the Clustering of Three-Way Data Introduction Fitting a Normal Mixture Model to Three-Way Data Clustering of Soybean Data Multidimensional Scaling Approach to the Analysis of Soybean Data References Appendix