This accessible but rigorous introduction is written for advanced undergraduates and beginning graduate students in data science, as well as researchers and practitioners. It shows how a statistical framework yields sound estimation, testing and prediction methods, using extensive data examples and providing R code for many methods.
This accessible but rigorous introduction is written for advanced undergraduates and beginning graduate students in data science, as well as researchers and practitioners. It shows how a statistical framework yields sound estimation, testing and prediction methods, using extensive data examples and providing R code for many methods.
Produktdetails
Produktdetails
Cambridge Series in Statistical and Probabilistic Mathematics
Charles Bouveyron is Full Professor of Statistics at Université Côte d'Azur and the Chair of Excellence in Data Science at Institut National de Recherche en Informatique et en Automatique (INRIA), Rocquencourt. He has published extensively on model-based clustering, particularly for networks and high-dimensional data.
Inhaltsangabe
1. Introduction 2. Model-based clustering: basic ideas 3. Dealing with difficulties 4. Model-based classification 5. Semi-supervised clustering and classification 6. Discrete data clustering 7. Variable selection 8. High-dimensional data 9. Non-Gaussian model-based clustering 10. Network data 11. Model-based clustering with covariates 12. Other topics List of R packages Bibliography Index.
1. Introduction 2. Model-based clustering: basic ideas 3. Dealing with difficulties 4. Model-based classification 5. Semi-supervised clustering and classification 6. Discrete data clustering 7. Variable selection 8. High-dimensional data 9. Non-Gaussian model-based clustering 10. Network data 11. Model-based clustering with covariates 12. Other topics List of R packages Bibliography Index.
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