Summary of Volume 1

Preface

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. 1.1. Introduction
  2. 1.2. Data understanding
  3. 1.3. Modeling
  4. 1.4. Findings
  5. 1.5. Conclusion
  6. 1.6. References

Chapter 2. Exploring Chronic Diseases’ Spatial Patterns: Thyroid Cancer in Sicilian Volcanic Areas

Francesca BITONTI and Angelo MAZZA

  1. 2.1. Introduction
  2. 2.2. Epidemiological data and territory
  3. 2.3. Methodology
    1. 2.3.1. Spatial inhomogeneity and spatial dependence
    2. 2.3.2. Standardized incidence ratio (SIR)
    3. 2.3.3. Local Moran’s I statistic
  4. 2.4. Spatial distribution of TC in eastern Sicily
    1. 2.4.1. SIR geographical variation
    2. 2.4.2. Estimate of the spatial attraction
  5. 2.5. Conclusion
  6. 2.6. References

Chapter 3. Analysis of Blockchain-based Databases in Web Applications

Orhun Ceng BOZO and Rüya ŞAMLI

  1. 3.1. Introduction
  2. 3.2. Background
    1. 3.2.1. Blockchain
    2. 3.2.2. Blockchain types
    3. 3.2.3. Blockchain-based web applications
    4. 3.2.4. Blockchain consensus algorithms
    5. 3.2.5. Other consensus algorithms
  3. 3.3. Analysis stack
    1. 3.3.1. Art Shop web application
    2. 3.3.2. SQL-based application
    3. 3.3.3. NoSQL-based application
    4. 3.3.4. Blockchain-based application
  4. 3.4. Analysis
    1. 3.4.1. Adding records
    2. 3.4.2. Query
    3. 3.4.3. Functionality
    4. 3.4.4. Security
  5. 3.5. Conclusion
  6. 3.6. References

Chapter 4. Optimization and Asymptotic Analysis of Insurance Models

Ekaterina BULINSKAYA

  1. 4.1. Introduction
  2. 4.2. Discrete-time model with reinsurance and bank loans
    1. 4.2.1. Model description
    2. 4.2.2. Optimization problem
    3. 4.2.3. Model stability
  3. 4.3. Continuous-time insurance model with dividends
    1. 4.3.1. Model description
    2. 4.3.2. Optimal barrier strategy
    3. 4.3.3. Special form of claim distribution
    4. 4.3.4. Numerical analysis
  4. 4.4. Conclusion and further research directions
  5. 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

  1. 5.1. Introduction
  2. 5.2. Data
  3. 5.3. Methodology
    1. 5.3.1. Main limit results
    2. 5.3.2. Block maxima method
    3. 5.3.3. Largest order statistics method
    4. 5.3.4. Estimation of other tail parameters
  4. 5.4. Results and conclusion
  5. 5.5. Acknowledgements
  6. 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

  1. 6.1. Introduction
  2. 6.2. Nearest neighbor methods
    1. 6.2.1. Background of the NN methods
    2. 6.2.2. The k-nearest neighbors method
    3. 6.2.3. The fixed-radius NN method
    4. 6.2.4. The kernel-NN method
    5. 6.2.5. Algorithms of the three considered NN methods
    6. 6.2.6. Parameter and distance metric selection
  3. 6.3. Experimental results
    1. 6.3.1. Dataset description
    2. 6.3.2. Variable selection and data splitting
    3. 6.3.3. Results
    4. 6.3.4. A discussion and comparison of results
  4. 6.4. Conclusion
  5. 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

  1. 7.1. Introduction
  2. 7.2. Methods
    1. 7.2.1. Participants
    2. 7.2.2. Instrument
    3. 7.2.3. Statistical analyses
  3. 7.3. Results
    1. 7.3.1. EFA results
    2. 7.3.2. CFA results
    3. 7.3.3. Scale construction and assessment
  4. 7.4. Conclusion
  5. 7.5. Funding
  6. 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

  1. 8.1. Introduction
  2. 8.2. Regression model for bounded responses
    1. 8.2.1. Augmentation
    2. 8.2.2. Main distributions on the bounded support
    3. 8.2.3. Inference and fit
  3. 8.3. Case studies
    1. 8.3.1. Stress data
    2. 8.3.2. Reading data
  4. 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

  1. 9.1. Introduction
  2. 9.2. Dirichlet and EFD distributions
  3. 9.3. Dirichlet and EFD regression models
    1. 9.3.1. Inference and fit
  4. 9.4. Simulation studies
    1. 9.4.1. Comments
  5. 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

  1. 10.1. Introduction
  2. 10.2. Asymptotic expansions of implied volatility
  3. 10.3. Performance of the asymptotic expansions
  4. 10.4. Calibration using the asymptotic expansions
    1. 10.4.1. A partial calibration procedure
    2. 10.4.2. Calibration to synthetic and market data
  5. 10.5. Conclusion and future work
  6. 10.6. References

Chapter 11. Performance Persistence of Polish Mutual Funds: Mobility Measures

Dariusz FILIP

  1. 11.1. Introduction
  2. 11.2. Literature review
  3. 11.3. Dataset and empirical design
  4. 11.4. Empirical results
  5. 11.5. Monthly perspective
  6. 11.6. Quarterly perspective
  7. 11.7. Yearly perspective
  8. 11.8. Conclusion
  9. 11.9. References

Chapter 12. Invariant Description for a Batch Version of the UCB Strategy with Unknown Control Horizon

Sergey GARBAR

  1. 12.1. Introduction
  2. 12.2. UCB strategy
  3. 12.3. Batch version of the strategy
  4. 12.4. Invariant description with a unit control horizon
  5. 12.5. Simulation results
  6. 12.6. Conclusion
  7. 12.7. Affiliations
  8. 12.8. References

Chapter 13. A New Non-monotonic Link Function for Beta Regressions

Gloria GHENO

  1. 13.1. Introduction
  2. 13.2. Model
  3. 13.3. Estimation
  4. 13.4. Comparison
  5. 13.5. Conclusion
  6. 13.6. References

Chapter 14. A Method of Big Data Collection and Normalization for Electronic Engineering Applications

Naveenbalaji GOWTHAMAN and Viranjay M. SRIVASTAVA

  1. 14.1. Introduction
  2. 14.2. Machine learning (ML) in electronic engineering
    1. 14.2.1. Data acquisition
    2. 14.2.2. Accessing the data repositories
    3. 14.2.3. Data storage and management
  3. 14.3. Electronic engineering applications – data science
  4. 14.4. Conclusion and future work
  5. 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

  1. 15.1. Introduction
  2. 15.2. Description of the method
    1. 15.2.1. The direct simulation method
    2. 15.2.2. Picard iterations
    3. 15.2.3. Runge–Kutta steps
  3. 15.3. Numerical examples
    1. 15.3.1. The Lorenz system
    2. 15.3.2. A combustion model
  4. 15.4. Conclusion
  5. 15.5. References

Chapter 16. Interpreting a Topological Measure of Complexity for Decision Boundaries

Alan HYLTON, Ian LIM, Michael MOY and Robert SHORT

  1. 16.1. Introduction
  2. 16.2. Persistent homology
  3. 16.3. Methodology
    1. 16.3.1. Neural networks and binary classification
    2. 16.3.2. Persistent homology of a decision boundary
    3. 16.3.3. Procedure
  4. 16.4. Experiments and results
    1. 16.4.1. Three-dimensional binary classification
    2. 16.4.2. Data divided by a hyperplane
  5. 16.5. Conclusion and discussion
  6. 16.6. References

Chapter 17. The Minimum Renyi’s Pseudodistance Estimators for Generalized Linear Models

María JAENADA and Leandro PARDO

  1. 17.1. Introduction
  2. 17.2. The minimum RP estimators for the GLM model: asymptotic distribution
  3. 17.3. Example: Poisson regression model
    1. 17.3.1. Real data application
  4. 17.4. Conclusion
  5. 17.5. Acknowledgments
  6. 17.6. Appendix.
    1. 17.6.1. Proof of Theorem 1
  7. 17.7. References

Chapter 18. Data Analysis based on Entropies and Measures of Divergence

Christos MESELIDIS, Alex KARAGRIGORIOU and Takis PAPAIOANNOU

  1. 18.1. Introduction
  2. 18.2. Divergence measures
  3. 18.3. Tests of fit based on Φ−divergence measures
  4. 18.4. Simulations
  5. 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

  1. 19.1. Introduction
  2. 19.2. Models and methodology
  3. 19.3. Data analysis
    1. 19.3.1. Data
    2. 19.3.2. Results
  4. 19.4. Conclusion
  5. 19.5. Acknowledgments
  6. 19.6. References

Chapter 20. Software Cost Estimation Using Machine Learning Algorithms

Sukran EBREN KARA and Rüya ŞAMLI

  1. 20.1. Introduction
  2. 20.2. Methodology
    1. 20.2.1. Dataset
    2. 20.2.2. Model
    3. 20.2.3. Evaluating the performance of the model
  3. 20.3. Results and discussion
  4. 20.4. Conclusion
  5. 20.5. References

Chapter 21. Monte Carlo Accuracy Evaluation of Laser Cutting Machine

Samuel KOSOLAPOV

  1. 21.1. Introduction
  2. 21.2. Mathematical model of a pintograph
  3. 21.3. Monte Carlo simulator
  4. 21.4. Simulation results
  5. 21.5. Conclusion
  6. 21.6. Acknowledgments
  7. 21.7. References

Chapter 22. Using Parameters of Piecewise Approximation by Exponents for Epidemiological Time Series Data Analysis

Samuel KOSOLAPOV

  1. 22.1. Introduction
  2. 22.2. Deriving equations for moving exponent parameters
  3. 22.3. Validation of derived equations by using synthetic data
  4. 22.4. Using derived equations to analyze real-life Covid-19 data
  5. 22.5. Conclusion
  6. 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

  1. 23.1. Introduction
  2. 23.2. Respiratory function physiology: ventilation–perfusion ratio
  3. 23.3. The basic principle of operation of artificial lung ventilation devices: patient monitoring parameters
  4. 23.4. The algorithm for monitoring the carbon emissions and oxygen consumption
  5. 23.5. Results
  6. 23.6. Conclusion
  7. 23.7. References

Part 4

Chapter 24. Approximate Bayesian Inference Using the Mean-Field Distribution

Antonin DELLA NOCE and Paul-Henry COURNÈDE

  1. 24.1. Introduction
  2. 24.2. Inference problem in a symmetric population system
    1. 24.2.1. Example of a symmetric system describing plant competition
    2. 24.2.2. Inference problem of the Schneider system, in a more general setting
  3. 24.3. Properties of the mean-field distribution
  4. 24.4. Mean-field approximated inference
    1. 24.4.1. Case of systems admitting a mean-field limit
  5. 24.5. Conclusion
  6. 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

  1. 25.1. Introduction and outline
  2. 25.2. Cubature formulae on Wiener space
    1. 25.2.1. A simple example of classical Monte Carlo estimates
    2. 25.2.2. Modern Monte Carlo estimates via cubature method
    3. 25.2.3. An application in the Black–Scholes SDE
    4. 25.2.4. Trajectories of the cubature formula of degree 5 on Wiener space
    5. 25.2.5. Trajectories of price process given in equation [25.7]
    6. 25.2.6. An application on path-dependent derivatives
    7. 25.2.7. Trinomial tree (model) via cubature formulae of degree 5
  3. 25.3. Interest-rate models and Hull–White one-factor model
    1. 25.3.1. Equilibrium models
    2. 25.3.2. No-arbitrage models
    3. 25.3.3. Forward rate models
    4. 25.3.4. Hull–White one-factor model
    5. 25.3.5. Discretization of the Hull–White model via Euler scheme
    6. 25.3.6. Hull–White model for bond prices
  4. 25.4. The Hull–White model via cubature method
    1. 25.4.1. Simulating SDE [25.15] and ODE [25.24]
    2. 25.4.2. The Hull–White interest-rate tree via iterated cubature formulae: some examples
  5. 25.5. Discussion and future works
  6. 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

  1. 26.1. Introduction
  2. 26.2. Materials and methods
    1. 26.2.1. Characteristics of the territory of the district
    2. 26.2.2. Demographic characteristics of the area
    3. 26.2.3. Characteristics of the district medical service
    4. 26.2.4. The procedure for collecting primary information on cases of diseases of the population with a new coronavirus infection
  3. 26.3. Results of the analysis of the incidence of acute respiratory viral infectious diseases, new coronavirus infection Covid-19 and community-acquired pneumonia
  4. 26.4. Conclusion
  5. 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

  1. 27.1. Introduction
  2. 27.2. Existing model review
  3. 27.3. Selection of a suitable model
  4. 27.4. Conclusion and future recommendations
  5. 27.5. References

Chapter 28. Reliability of a Double Redundant System Under the Full Repair Scenario

Vladimir RYKOV and Nika IVANOVA

  1. 28.1. Introduction
  2. 28.2. Problem statement, assumptions and notations
  3. 28.3. Reliability function
  4. 28.4. Time-dependent system state probabilities
    1. 28.4.1. General representation of t.d.s.p.s
    2. 28.4.2. T.d.s.p.s in a separate regeneration period
  5. 28.5. Steady-state probabilities
  6. 28.6. Conclusion
  7. 28.7. References

Chapter 29. Predicting Changes in Depression Levels Following the European Economic Downturn of 2008

Eleni SERAFETINIDOU and Georgia VERROPOULOU

  1. 29.1. Introduction
    1. 29.1.1. Aims of the study
  2. 29.2. Data and methods
    1. 29.2.1. Sample
    2. 29.2.2. Measures
  3. 29.3. Results
    1. 29.3.1. Descriptive findings
    2. 29.3.2. Non-respondents compared to respondents at baseline (wave 2)
    3. 29.3.3. Descriptive findings for respondents – analysis by gender
    4. 29.3.4. Findings regarding decreasing depression levels – analysis for the total sample and by gender
    5. 29.3.5. Findings regarding increasing depression levels – analysis for the total sample and by gender
  4. 29.4. Discussion
  5. 29.5. Conclusion
  6. 29.6. Acknowledgments
  7. 29.7. References
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