| 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 |
| Mixture Likelihood Approach to the Clustering of Three-Way Data / Example 1： |
| 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 |
| 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 |
