Konstantinos N. ZAFEIRIS, Yiannis DIMOTIKALIS, Christos H. SKIADAS, Alex KARAGRIGORIOU and Christiana KARAGRIGORIOU-VONTA
Part 1
Chapter 1. Performance of Evaluation of Diagnosis of Various Thyroid Diseases Using Machine Learning Techniques
Burcu Bektas GÜNEŞ, Evren BURSUK and Rüya ŞAMLI
1.1. Introduction
1.2. Data understanding
1.3. Modeling
1.4. Findings
1.5. Conclusion
1.6. References
Chapter 2. Exploring Chronic Diseases’ Spatial Patterns: Thyroid Cancer in Sicilian Volcanic Areas
Francesca BITONTI and Angelo MAZZA
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
Chapter 3. Analysis of Blockchain-based Databases in Web Applications
Orhun Ceng BOZO and Rüya ŞAMLI
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
Chapter 4. Optimization and Asymptotic Analysis of Insurance Models
Ekaterina BULINSKAYA
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
Chapter 5. Statistical Analysis of Traffic Volume in the 25 de Abril Bridge
Frederico CAEIRO, Ayana MATEUS and Conceicao VEIGA de ALMEIDA
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
Chapter 6. Predicting the Risk of Gestational Diabetes Mellitus through Nearest Neighbor Classification
Louisa TESTA, Mark A. CARUANA, Maria KONTORINAKI and Charles SAVONA-VENTURA
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
Chapter 7. Political Trust in National Institutions: The Significance of Items’ Level of Measurement in the Validation of Constructs
Anastasia CHARALAMPI, Eva TSOUPAROPOULOU, Joanna TSIGANOU and Catherine MICHALOPOULOU
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
Chapter 8. The State of the Art in Flexible Regression Models for Univariate Bounded Responses
Agnese Maria DI BRISCO, Roberto ASCARI, Sonia MIGLIORATI and Andrea ONGARO
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 fit
8.3. Case studies
8.3.1. Stress data
8.3.2. Reading data
8.4. References
Chapter 9. Simulation Studies for a Special Mixture Regression Model with Multivariate Responses on the Simplex
Agnese Maria DI BRISCO, Roberto ASCARI, Sonia MIGLIORATI and Andrea ONGARO
9.1. Introduction
9.2. Dirichlet and EFD distributions
9.3. Dirichlet and EFD regression models
9.3.1. Inference and fit
9.4. Simulation studies
9.4.1. Comments
9.5. References
Part 2
Chapter 10. Numerical Studies of Implied Volatility Expansions Under the Gatheral Model
Marko DIMITROV, Mohammed ALBUHAYRI, Ying NI and Anatoliy MALYARENKO
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
Chapter 11. Performance Persistence of Polish Mutual Funds: Mobility Measures
Dariusz FILIP
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
Chapter 12. Invariant Description for a Batch Version of the UCB Strategy with Unknown Control Horizon
Sergey GARBAR
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
Chapter 13. A New Non-monotonic Link Function for Beta Regressions
Gloria GHENO
13.1. Introduction
13.2. Model
13.3. Estimation
13.4. Comparison
13.5. Conclusion
13.6. References
Chapter 14. A Method of Big Data Collection and Normalization for Electronic Engineering Applications
Naveenbalaji GOWTHAMAN and Viranjay M. SRIVASTAVA
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
Chapter 15. Stochastic Runge–Kutta Solvers Based on Markov Jump Processes and Applications to Non-autonomous Systems of Differential Equations
Flavius GUIAŞ
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
Chapter 16. Interpreting a Topological Measure of Complexity for Decision Boundaries
Alan HYLTON, Ian LIM, Michael MOY and Robert SHORT
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
Chapter 17. The Minimum Renyi’s Pseudodistance Estimators for Generalized Linear Models
María JAENADA and Leandro PARDO
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
Chapter 18. Data Analysis based on Entropies and Measures of Divergence
Christos MESELIDIS, Alex KARAGRIGORIOU and Takis PAPAIOANNOU
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
Chapter 19. Geographically Weighted Regression for Official Land Prices and their Temporal Variation in Tokyo
Yuta KANNO and Takayuki SHIOHAMA
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
Chapter 20. Software Cost Estimation Using Machine Learning Algorithms
Sukran EBREN KARA and Rüya ŞAMLI
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
Chapter 21. Monte Carlo Accuracy Evaluation of Laser Cutting Machine
Samuel KOSOLAPOV
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
Chapter 22. Using Parameters of Piecewise Approximation by Exponents for Epidemiological Time Series Data Analysis
Samuel KOSOLAPOV
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
Chapter 23. The Correlation Between Oxygen Consumption and Excretion of Carbon Dioxide in the Human Respiratory Cycle
Anatoly KOVALENKO, Konstantin LEBEDINSKII and Verangelina MOLOSHNEVA
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
Chapter 24. Approximate Bayesian Inference Using the Mean-Field Distribution
Antonin DELLA NOCE and Paul-Henry COURNÈDE
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
Chapter 25. Pricing Financial Derivatives in the Hull–White Model Using Cubature Methods on Wiener Space
Hossein NOHROUZIAN, Anatoliy MALYARENKO and Ying NI
25.1. Introduction and outline
25.2. Cubature formulae on Wiener space
25.2.1. A simple example of classical Monte Carlo estimates
25.2.2. Modern Monte Carlo estimates via cubature method
25.2.3. An application in the Black–Scholes SDE
25.2.4. Trajectories of the cubature formula of degree 5 on Wiener space
25.2.5. Trajectories of price process given in equation [25.7]
25.2.6. An application on path-dependent derivatives
25.2.7. Trinomial tree (model) via cubature formulae of degree 5
25.3. Interest-rate models and Hull–White one-factor model
25.3.1. Equilibrium models
25.3.2. No-arbitrage models
25.3.3. Forward rate models
25.3.4. Hull–White one-factor model
25.3.5. Discretization of the Hull–White model via Euler scheme
25.3.6. Hull–White model for bond prices
25.4. The Hull–White model via cubature method
25.4.1. Simulating SDE [25.15] and ODE [25.24]
25.4.2. The Hull–White interest-rate tree via iterated cubature formulae: some examples
25.5. Discussion and future works
25.6. References
Chapter 26. Differences in the Structure of Infectious Morbidity of the Population during the First and Second Half of 2020 in St. Petersburg
Vasilii OREL, Olga NOSYREVA, Tatiana BULDAKOVA, Natalya GUREVA, Viktoria SMIRNOVA, Andrey KIM and Lubov SHARAFUTDINOVA
26.1. Introduction
26.2. Materials and methods
26.2.1. Characteristics of the territory of the district
26.2.2. Demographic characteristics of the area
26.2.3. Characteristics of the district medical service
26.2.4. The procedure for collecting primary information on cases of diseases of the population with a new coronavirus infection
26.3. Results of the analysis of the incidence of acute respiratory viral infectious diseases, new coronavirus infection Covid-19 and community-acquired pneumonia
26.4. Conclusion
26.5. References
Chapter 27. High Speed and Secured Network Connectivity for Higher Education Institutions Using Software Defined Networks
Lincoln S. PETER and Viranjay M. SRIVASTAVA
27.1. Introduction
27.2. Existing model review
27.3. Selection of a suitable model
27.4. Conclusion and future recommendations
27.5. References
Chapter 28. Reliability of a Double Redundant System Under the Full Repair Scenario
Vladimir RYKOV and Nika IVANOVA
28.1. Introduction
28.2. Problem statement, assumptions and notations
28.3. Reliability function
28.4. Time-dependent system state probabilities
28.4.1. General representation of t.d.s.p.s
28.4.2. T.d.s.p.s in a separate regeneration period
28.5. Steady-state probabilities
28.6. Conclusion
28.7. References
Chapter 29. Predicting Changes in Depression Levels Following the European Economic Downturn of 2008
Eleni SERAFETINIDOU and Georgia VERROPOULOU
29.1. Introduction
29.1.1. Aims of the study
29.2. Data and methods
29.2.1. Sample
29.2.2. Measures
29.3. Results
29.3.1. Descriptive findings
29.3.2. Non-respondents compared to respondents at baseline (wave 2)
29.3.3. Descriptive findings for respondents – analysis by gender
29.3.4. Findings regarding decreasing depression levels – analysis for the total sample and by gender
29.3.5. Findings regarding increasing depression levels – analysis for the total sample and by gender