Object Oriented Data Analysis (OODA) provides a useful general framework for the consideration of many types of Complex Data. It is deliberately intended to be particularly useful in the analysis of data in complicated situations which are typically not easily represented as an unconstrained matrix of numbers.
Object Oriented Data Analysis (OODA) provides a useful general framework for the consideration of many types of Complex Data. It is deliberately intended to be particularly useful in the analysis of data in complicated situations which are typically not easily represented as an unconstrained matrix of numbers.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
Produktdetails
Produktdetails
Chapman & Hall/CRC Monographs on Statistics and Applied Probability
J. S. Marron is the Amos Hawley Distinguished Professor of Statistics, Professor of Biostatistics, Adjunct Professor of Computer Science, Faculty Member of the Bioinformatics and Computational Biology Curriculum and Research Member of the Lineberger Cancer Center and the Computational Medicine Program, at the University of North Carolina, Chapel Hill. Ian L. Dryden is Professor of Statistics in the School of Mathematical Sciences at the University of Nottingham, has served as Head of School, and is joint author of the acclaimed book Statistical Shape Analysis.
Inhaltsangabe
1. What is OODA? 2. Breadth of OODA 3. Data Object Definition 4. Exploratory and Confirmatory Analyses 5. OODA P6 6. Data Visualization 7. Distance Based Methods 8. Manifold Data Analysis 9. FDA Curve Registration 10. Graph Structured Data Objects 11. Classification - Supervised Learning 12. Clustering - Unsupervised Learning 13. High Dimensional Inference 14. High Dimensional Asymptotics 15. Smoothing and SiZer 16. Robust Methods 17. PCA Details and Variants 18. OODA Context and Related Areas
1. What is OODA? 2. Breadth of OODA 3. Data Object Definition 4. Exploratory and Confirmatory Analyses 5. OODA P6 6. Data Visualization 7. Distance Based Methods 8. Manifold Data Analysis 9. FDA Curve Registration 10. Graph Structured Data Objects 11. Classification - Supervised Learning 12. Clustering - Unsupervised Learning 13. High Dimensional Inference 14. High Dimensional Asymptotics 15. Smoothing and SiZer 16. Robust Methods 17. PCA Details and Variants 18. OODA Context and Related Areas
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