Data analysis and applications 3 : computational, classification, financial, statistical and stochastic methods /

Data analysis as an area of importance has grown exponentially, especially during the past couple of decades. This can be attributed to a rapidly growing computer industry and the wide applicability of computational techniques, in conjunction with new advances of analytic tools. This being the case,...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Makrides, Andreas (Lecturer in Mathematics and Statistics) (Editor ), Karagrigoriou, Alex (Editor ), Skiadas, Christos H. (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: London : Hoboken, NJ, USA : ISTE ; John Wiley and Sons, Inc., 2020.
Colección:Innovation, entrepreneurship and management series. Big data, artificial intelligence and data analysis set ; v. 5.
Temas:
Quantitative research.
Data mining.
Data Mining
Recherche quantitative.
Exploration de données (Informatique)
Data mining
Quantitative research
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)
Tabla de Contenidos:
  • Cover
  • Half-Title Page
  • Title Page
  • Copyright Page
  • Contents
  • Preface
  • PART 1: Computational Data Analysis and Methods
  • 1. Semi-supervised Learning Based on Distributionally Robust Optimization
  • 1.1. Introduction
  • 1.2. Alternative semi-supervised learning procedures
  • 1.3. Semi-supervised learning based on DRO
  • 1.3.1. Defining the optimal transport discrepancy
  • 1.3.2. Solving the SSL-DRO formulation
  • 1.4. Error improvement of our SSL-DRO formulation
  • 1.5. Numerical experiments
  • 1.6. Discussion on the size of the uncertainty set
  • 1.7. Conclusion
  • 1.8. Appendix: supplementary material: technical details for theorem 1.1
  • 1.8.1. Assumptions of theorem 1.1
  • 1.8.2. Revisit theorem 1.1
  • 1.8.3. Proof of theorem 1.1
  • 1.9. References
  • 2. Updating of PageRank in Evolving Treegraphs
  • 2.1. Introduction
  • 2.2. Abbreviations and definitions
  • 2.3. Finding components
  • 2.3.1. Isolation of vertices in the graph
  • 2.3.2. Keeping track of every vertex in the components
  • 2.4. Maintaining the level of cycles
  • 2.5. Calculating PageRank
  • 2.6. PageRank of a tree with at least a cycle after addition of an edge
  • 5. Investigation on Life Satisfaction Through (Stratified) Chain Regression Graph Models
  • 5.1. Introduction
  • 5.2. Methodology
  • 5.3. Application
  • 5.3.1. Survey on multiple aims analysis
  • 5.4. Conclusion
  • 5.5. References
  • PART 2: Classification Data Analysis and Methods
  • 6. Selection of Proximity Measures for a Topological Correspondence Analysis
  • 6.1. Introduction
  • 6.2. Topological correspondence
  • 6.2.1. Comparison and selection of proximity measures
  • 6.2.2. Statistical comparisons between two proximity measures
  • 6.3. Application to real data and empirical results

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