Data analysis and related applications . Volume 1 : computational, algorithmic and applied economic data analysis / / Konstantinos N Zafeiris [and four others], editors

• Pubblicazione: Hoboken, NJ : , : John Wiley and Sons Inc., , [2022]; ©2022
• Descrizione fisica: 1 online resource (478 pages)
• Disciplina: 001.42
• Soggetto topico: Mathematical statistics; Electronic data processing; Quantitative research
• Persona (resp. second.): ZafeirisKonstantinos N
• Nota di contenuto: Cover -- Half-Title Page -- Title Page -- Copyright Page -- Contents -- Preface -- Part 1 -- 1. Performance of Evaluation of Diagnosis of Various Thyroid Diseases Using Machine Learning Techniques -- 1.1. Introduction -- 1.2. Data understanding -- 1.3. Modeling -- 1.4. Findings -- 1.5. Conclusion -- 1.6. References -- 2. Exploring Chronic Diseases' Spatial Patterns: Thyroid Cancer in Sicilian Volcanic Areas -- 2.1. Introduction -- 2.2. Epidemiological data and territory -- 2.3. Methodology -- 2.3.1. Spatial inhomogeneity and spatial dependence -- 2.3.2. Standardized incidence ratio (SIR) -- 2.3.3. Local Moran's I statistic -- 2.4. Spatial distribution of TC in eastern Sicily -- 2.4.1. SIR geographical variation -- 2.4.2. Estimate of the spatial attraction -- 2.5. Conclusion -- 2.6. References -- 3. Analysis of Blockchain-based Databases in Web Applications -- 3.1. Introduction -- 3.2. Background -- 3.2.1. Blockchain -- 3.2.2. Blockchain types -- 3.2.3. Blockchain-based web applications -- 3.2.4. Blockchain consensus algorithms -- 3.2.5. Other consensus algorithms -- 3.3. Analysis stack -- 3.3.1. Art Shop web application -- 3.3.2. SQL-based application -- 3.3.3. NoSQL-based application -- 3.3.4. Blockchain-based application -- 3.4. Analysis -- 3.4.1. Adding records -- 3.4.2. Query -- 3.4.3. Functionality -- 3.4.4. Security -- 3.5. Conclusion -- 3.6. References -- 4. Optimization and Asymptotic Analysis of Insurance Models -- 4.1. Introduction -- 4.2. Discrete-time model with reinsurance and bank loans -- 4.2.1. Model description -- 4.2.2. Optimization problem -- 4.2.3. Model stability -- 4.3. Continuous-time insurance model with dividends -- 4.3.1. Model description -- 4.3.2. Optimal barrier strategy -- 4.3.3. Special form of claim distribution -- 4.3.4. Numerical analysis -- 4.4. Conclusion and further research directions -- 4.5. References. ; 5. Statistical Analysis of TrafficVolume in the 25 de Abril Bridge -- 5.1. Introduction -- 5.2. Data -- 5.3. Methodology -- 5.3.1. Main limit results -- 5.3.2. Block maxima method -- 5.3.3. Largest order statistics method -- 5.3.4. Estimation of other tail parameters -- 5.4. Results and conclusion -- 5.5. Acknowledgements -- 5.6. References -- 6. Predicting the Risk of Gestational Diabetes Mellitus through Nearest Neighbor Classification -- 6.1. Introduction -- 6.2. Nearest neighbor methods -- 6.2.1. Background of the NN methods -- 6.2.2. The k-nearest neighbors method -- 6.2.3. The fixed-radius NN method -- 6.2.4. The kernel-NN method -- 6.2.5. Algorithms of the three considered NN methods -- 6.2.6. Parameter and distance metric selection -- 6.3. Experimental results -- 6.3.1. Dataset description -- 6.3.2. Variable selection and data splitting -- 6.3.3. Results -- 6.3.4. A discussion and comparison of results -- 6.4. Conclusion -- 6.5. References -- 7. Political Trust in National Institutions: The Significance of Items' Level of Measurement in the Validation of Constructs -- 7.1. Introduction -- 7.2. Methods -- 7.2.1. Participants -- 7.2.2. Instrument -- 7.2.3. Statistical analyses -- 7.3. Results -- 7.3.1. EFA results -- 7.3.2. CFA results -- 7.3.3. Scale construction and assessment -- 7.4. Conclusion -- 7.5. Funding -- 7.6. References -- 8. The State of the Art in Flexible Regression Models for Univariate Bounded Responses -- 8.1. Introduction -- 8.2. Regression model for bounded responses -- 8.2.1. Augmentation -- 8.2.2. Main distributions on the bounded support -- 8.2.3. Inference and -- 8.3. Case studies -- 8.3.1. Stress data -- 8.3.2. Reading data -- 8.4. References -- 9. Simulation Studies for a Special Mixture Regression Model with Multivariate Responses on the Simplex -- 9.1. Introduction -- 9.2. Dirichlet and EFD distributions. ; 9.3. Dirichlet and EFD regression models -- 9.3.1. Inference and -- 9.4. Simulation studies -- 9.4.1. Comments -- 9.5. References -- Part 2 -- 10. Numerical Studies of Implied Volatility Expansions Under the Gatheral Model -- 10.1. Introduction -- 10.2. Asymptotic expansions of implied volatility -- 10.3. Performance of the asymptotic expansions -- 10.4. Calibration using the asymptotic expansions -- 10.4.1. A partial calibration procedure -- 10.4.2. Calibration to synthetic and market data -- 10.5. Conclusion and future work -- 10.6. References -- 11. Performance Persistence of Polish Mutual Funds: Mobility Measures -- 11.1. Introduction -- 11.2. Literature review -- 11.3. Dataset and empirical design -- 11.4. Empirical results -- 11.5. Monthly perspective -- 11.6. Quarterly perspective -- 11.7. Yearly perspective -- 11.8. Conclusion -- 11.9. References -- 12. Invariant Description for a Batch Version of the UCB Strategy with Unknown Control Horizon -- 12.1. Introduction -- 12.2. UCB strategy -- 12.3. Batch version of the strategy -- 12.4. Invariant description with a unit control horizon -- 12.5. Simulation results -- 12.6. Conclusion -- 12.7. Affiliations -- 12.8. References -- 13. A New Non-monotonic Link Function for Beta Regressions -- 13.1. Introduction -- 13.2. Model -- 13.3. Estimation -- 13.4. Comparison -- 13.5. Conclusion -- 13.6. References -- 14. A Method of Big Data Collection and Normalization for Electronic Engineering Applications -- 14.1. Introduction -- 14.2. Machine learning (ML) in electronic engineering -- 14.2.1. Data acquisition -- 14.2.2. Accessing the data repositories -- 14.2.3. Data storage and management -- 14.3. Electronic engineering applications - data science -- 14.4. Conclusion and future work -- 14.5. References. ; 15. Stochastic Runge-Kutta Solvers Based on Markov Jump Processes and Applications to Non-autonomous Systems of Differential Equations -- 15.1. Introduction -- 15.2. Description of the method -- 15.2.1. The direct simulation method -- 15.2.2. Picard iterations -- 15.2.3. Runge-Kutta steps -- 15.3. Numerical examples -- 15.3.1. The Lorenz system -- 15.3.2. A combustion model -- 15.4. Conclusion -- 15.5. References -- 16. Interpreting a Topological Measure of Complexity for Decision Boundaries -- 16.1. Introduction -- 16.2. Persistent homology -- 16.3. Methodology -- 16.3.1. Neural networks and binary classification -- 16.3.2. Persistent homology of a decision boundary -- 16.3.3. Procedure -- 16.4. Experiments and results -- 16.4.1. Three-dimensional binary classification -- 16.4.2. Data divided by a hyperplane -- 16.5. Conclusion and discussion -- 16.6. References -- 17. The Minimum Renyi's Pseudodistance Estimators for Generalized Linear Models -- 17.1. Introduction -- 17.2. The minimum RP estimators for the GLM model: asymptotic distribution -- 17.3. Example: Poisson regression model -- 17.3.1. Real data application -- 17.4. Conclusion -- 17.5. Acknowledgments -- 17.6. Appendix -- 17.6.1. Proof of Theorem 1 -- 17.7. References -- 18. Data Analysis based on Entropies and Measures of Divergence -- 18.1. Introduction -- 18.2. Divergence measures -- 18.3. Tests of fit based on Φ−divergence measures -- 18.4. Simulations -- 18.5. References -- Part 3 -- 19. Geographically Weighted Regression for Official Land Prices and their Temporal Variation in Tokyo -- 19.1. Introduction -- 19.2. Models and methodology -- 19.3. Data analysis -- 19.3.1. Data -- 19.3.2. Results -- 19.4. Conclusion -- 19.5. Acknowledgments -- 19.6. References -- 20. Software Cost Estimation Using Machine Learning Algorithms -- 20.1. Introduction -- 20.2. Methodology -- 20.2.1. Dataset. ; 20.2.2. Model -- 20.2.3. Evaluating the performance of the model -- 20.3. Results and discussion -- 20.4. Conclusion -- 20.5. References -- 21. Monte Carlo Accuracy Evaluation of Laser Cutting Machine -- 21.1. Introduction -- 21.2. Mathematical model of a pintograph -- 21.3. Monte Carlo simulator -- 21.4. Simulation results -- 21.5. Conclusion -- 21.6. Acknowledgments -- 21.7. References -- 22. Using Parameters of Piecewise Approximation by Exponents for Epidemiological Time Series Data Analysis -- 22.1. Introduction -- 22.2. Deriving equations for moving exponent parameters -- 22.3. Validation of derived equations by using synthetic data -- 22.4. Using derived equations to analyze real-life Covid-19 data -- 22.5. Conclusion -- 22.6. References -- 23. The Correlation Between Oxygen Consumption and Excretion of Carbon Dioxide in the Human Respiratory Cycle -- 23.1. Introduction -- 23.2. Respiratory function physiology: ventilation-perfusion ratio -- 23.3. The basic principle of operation of artificial lung ventilation devices: patient monitoring parameters -- 23.4. The algorithm for monitoring the carbon emissions and oxygen consumption -- 23.5. Results -- 23.6. Conclusion -- 23.7. References -- Part 4 -- 24. Approximate Bayesian Inference Using the Mean-Field Distribution -- 24.1. Introduction -- 24.2. Inference problem in a symmetric population system -- 24.2.1. Example of a symmetric system describing plant competition -- 24.2.2. Inference problem of the Schneider system, in a more general setting -- 24.3. Properties of the mean-field distribution -- 24.4. Mean-field approximated inference -- 24.4.1. Case of systems admitting a mean-field limit -- 24.5. Conclusion -- 24.6. References -- 25. Pricing Financial Derivatives in the Hull-White Model Using Cubature Methods on Wiener Space -- 25.1. Introduction and outline. ; 25.2. Cubature formulae on Wiener space.
• Titolo autorizzato: Data analysis and related applications
• ISBN: 1-394-16551-X; 1-394-16549-8
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910643817003321
• Lo trovi qui: Univ. Federico II
• Serie: Innovation, entrepreneurship and management series. Big data, artificial intelligence and data analysis set ; ; Volume 9.