Details

Autor(en) / Beteiligte

• Zafeiris, Konstantinos N,

Titel

Data analysis and related applications : computational, algorithmic and applied economic data analysis. Volume 1

Ort / Verlag

Hoboken, NJ : John Wiley and Sons Inc.,

Erscheinungsjahr

[2022]

Link zum Volltext

• Wiley Online Library - AutoHoldings Books

Beschreibungen/Notizen

• 7.1. Introduction
• 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.
• 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, high-quality publications to cover the recent advances in all fields of science and engineering. This book is a collective work by a number of leading scientists, computer experts, analysts, engineers, mathematicians, probabilists and statisticians who have been working at the forefront of data analysis and related applications. The chapters of this collaborative work represent a cross-section of current concerns, developments and research interests in the above scientific areas. The collected material has been divided into appropriate sections to provide the reader with both theoretical and applied information on data analysis methods, models and techniques, along with related applications.
• Description based on print version record.