Data analysis and related applications. 2, Multivariate, health and demographic data analysis /

The scientific field of data analysis is constantly expanding due to the rapid growth of the computer industry and the wide applicability of computational and algorithmic techniques, in conjunction with new advances in statistical, stochastic and analytic tools. There is a constant need for new, hig...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Zafeiris, Konstantinos N. (Editor ), Skiadas, Christos H. (Editor ), Dimotikalis, Yiannis (Editor ), Karagrigoriou, Alex (Editor ), Karagrigoriou-Vonta, Christiana (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: London : Hoboken, NJ : ISTE Ltd ; John Wiley & Sons, Inc., 2022.
Colección:Innovation, entrepreneurship and management series. Big data, artificial intelligence and data analysis set ; v. 10.
Temas:
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
  • 1. A Topological Clustering of Variables
  • 1.1. Introduction
  • 1.2. Topological context
  • 1.2.1. Reference adjacency matrices
  • 1.2.2. Quantitative variables
  • 1.2.3. Qualitative variables
  • 1.2.4. Mixed variables
  • 1.3. Topological clustering of variables
  • selective review
  • 1.4. Illustration on real data of simple examples
  • 1.4.1. Case of a set of quantitative variables
  • 1.4.2. Case of a set of qualitative variables
  • 1.4.3. Case of a set of mixed variables
  • 1.5. Conclusion
  • 1.6. Appendix
  • 1.7. References
  • 2. A New Regression Model for Count Compositions
  • 2.1. Introduction
  • 2.1.1. Distributions for count vectors
  • 2.2. Regression models and Bayesian inference
  • 2.3. Simulation studies
  • 2.3.1. Fitting study
  • 2.3.2. Excess of zeroes
  • 2.4. Application to real electoral data
  • 2.5. References
  • 3. Intergenerational Class Mobility in Greece with Evidence from EU-SILC
  • 3.1. Introduction
  • 3.2. Data and methods
  • 3.3. The trends of class mobility between different birth cohorts
  • 3.4. Conclusion
  • 3.5. References
  • 4. Capturing School-to-Work Transitions Using Data from the First European Graduate Survey
  • 4.1. Introduction
  • 4.2. Data and methodology
  • 4.3. Results
  • 4.4. Conclusion
  • 4.5. References
  • 5. A Cluster Analysis Approach for Identifying Precarious Workers
  • 5.1. Introduction
  • 5.2. Data and methodology
  • 5.3. Results
  • 5.4. Conclusion and discussion
  • 5.4.1. Declarations
  • 5.5. References
  • 6. American Option Pricing Under a Varying Economic Situation Using Semi-Markov Decision Process
  • 6.1. Introduction
  • 6.2. American option pricing
  • 6.3. Exercising strategies
  • 6.3.1. Setting parameter
  • 6.3.2. Relationship between the American option price and economic situation i
  • 6.3.3. Relationship between the American option price and the asset price s
  • 6.3.4. Relationship between the American option price and maturity T
  • 6.3.5. Relationship between the American option price and transition probabilities P
  • 6.3.6. Consideration of the optimal exercise region
  • 6.4. Conclusion
  • 6.5. References
  • 7. The Implementation of Hierarchical Classifications and Cochran's Rule in the Analysis of Social Data
  • 7.1. Introduction
  • 7.2. Methods
  • 7.3. Results
  • 7.4. Conclusion
  • 7.5. References
  • 8. Dynamic Optimization with Tempered Stable Subordinators for Modeling River Hydraulics
  • 8.1. Introduction
  • 8.2. Mathematical model
  • 8.3. Optimization problem
  • 8.4. HJBI equation: formulation and solution
  • 8.5. Concluding remarks
  • 8.6. Acknowledgments
  • 8.7. References
  • PART 2
  • 9. Predicting Event Counts in Event-Driven Clinical Trials Accounting for Cure and Ongoing Recruitment
  • 9.1. Introduction
  • 9.2. Modeling the process of event occurrence
  • 9.2.1. Estimating parameters of the model

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