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 |