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.

Table of Contents:
Preface xvii Konstantinos N. ZAFEIRIS, Yiannis DIMOTIKALIS, Christos H. SKIADAS, Alex KARAGRIGORIOU and Christiana KARAGRIGORIOU-VONTA Part 1 1 Chapter 1. Performance of Evaluation of Diagnosis of Various Thyroid Diseases Using Machine Learning Techniques 3 Burcu Bektas GÜNEŞ, Evren BURSUK and Rüya ŞAMLI 1.1. Introduction 3 1.2. Data understanding 5 1.3. Modeling 6 1.4. Findings 8 1.5. Conclusion 10 1.6. References 10 Chapter 2. Exploring Chronic Diseases’ Spatial Patterns: Thyroid Cancer in Sicilian Volcanic Areas 13 Francesca BITONTI and Angelo MAZZA 2.1. Introduction 14 2.2. Epidemiological data and territory 16 2.3. Methodology 18 2.3.1. Spatial inhomogeneity and spatial dependence 18 2.3.2. Standardized incidence ratio (SIR) 19 2.3.3. Local Moran’s I statistic 21 2.4. Spatial distribution of TC in eastern Sicily 22 2.4.1. SIR geographical variation 22 2.4.2. Estimate of the spatial attraction 24 2.5. Conclusion 25 2.6. References 26 Chapter 3. Analysis of Blockchain-based Databases in Web Applications 31 Orhun Ceng BOZO and Rüya ŞAMLI 3.1. Introduction 31 3.2. Background 32 3.2.1. Blockchain 32 3.2.2. Blockchain types 32 3.2.3. Blockchain-based web applications 33 3.2.4. Blockchain consensus algorithms 33 3.2.5. Other consensus algorithms 34 3.3. Analysis stack 34 3.3.1. Art Shop web application 34 3.3.2. SQL-based application 34 3.3.3. NoSQL-based application 35 3.3.4. Blockchain-based application 35 3.4. Analysis 36 3.4.1. Adding records 36 3.4.2. Query 38 3.4.3. Functionality 39 3.4.4. Security 39 3.5. Conclusion 41 3.6. References 41 Chapter 4. Optimization and Asymptotic Analysis of Insurance Models 43 Ekaterina BULINSKAYA 4.1. Introduction 43 4.2. Discrete-time model with reinsurance and bank loans 44 4.2.1. Model description 44 4.2.2. Optimization problem 45 4.2.3. Model stability 46 4.3. Continuous-time insurance model with dividends 48 4.3.1. Model description 48 4.3.2. Optimal barrier strategy 49 4.3.3. Special form of claim distribution 50 4.3.4. Numerical analysis 54 4.4. Conclusion and further research directions 55 4.5. References 56 Chapter 5. Statistical Analysis of Traffic Volume in the 25 de Abril Bridge 57 Frederico CAEIRO, Ayana MATEUS and Conceicao VEIGA de ALMEIDA 5.1. Introduction 57 5.2. Data 58 5.3. Methodology 60 5.3.1. Main limit results 60 5.3.2. Block maxima method 61 5.3.3. Largest order statistics method 62 5.3.4. Estimation of other tail parameters 63 5.4. Results and conclusion 63 5.5. Acknowledgements 65 5.6. References 65 Chapter 6. Predicting the Risk of Gestational Diabetes Mellitus through Nearest Neighbor Classification 67 Louisa TESTA, Mark A. CARUANA, Maria KONTORINAKI and Charles SAVONA-VENTURA 6.1. Introduction 67 6.2. Nearest neighbor methods 69 6.2.1. Background of the NN methods 69 6.2.2. The k-nearest neighbors method 70 6.2.3. The fixed-radius NN method 70 6.2.4. The kernel-NN method 71 6.2.5. Algorithms of the three considered NN methods 72 6.2.6. Parameter and distance metric selection 74 6.3. Experimental results 75 6.3.1. Dataset description 75 6.3.2. Variable selection and data splitting 75 6.3.3. Results 76 6.3.4. A discussion and comparison of results 78 6.4. Conclusion 79 6.5. References 79 Chapter 7. Political Trust in National Institutions: The Significance of Items’ Level of Measurement in the Validation of Constructs 81 Anastasia CHARALAMPI, Eva TSOUPAROPOULOU, Joanna TSIGANOU and Catherine MICHALOPOULOU 7.1. Introduction 82 7.2. Methods 83 7.2.1. Participants 83 7.2.2. Instrument 84 7.2.3. Statistical analyses 85 7.3. Results 87 7.3.1. EFA results 87 7.3.2. CFA results 88 7.3.3. Scale construction and assessment 91 7.4. Conclusion 94 7.5. Funding 95 7.6. References 95 Chapter 8. The State of the Art in Flexible Regression Models for Univariate Bounded Responses 99 Agnese Maria DI BRISCO, Roberto ASCARI, Sonia MIGLIORATI and Andrea ONGARO 8.1. Introduction 100 8.2. Regression model for bounded responses 101 8.2.1. Augmentation 102 8.2.2. Main distributions on the bounded support 103 8.2.3. Inference and fit 106 8.3. Case studies 107 8.3.1. Stress data 107 8.3.2. Reading data 110 8.4. References 112 Chapter 9. Simulation Studies for a Special Mixture Regression Model with Multivariate Responses on the Simplex 115 Agnese Maria DI BRISCO, Roberto ASCARI, Sonia MIGLIORATI and Andrea ONGARO 9.1. Introduction 115 9.2. Dirichlet and EFD distributions 116 9.3. Dirichlet and EFD regression models 118 9.3.1. Inference and fit 118 9.4. Simulation studies 119 9.4.1. Comments 124 9.5. References 131 Part 2 133 Chapter 10. Numerical Studies of Implied Volatility Expansions Under the Gatheral Model 135 Marko DIMITROV, Mohammed ALBUHAYRI, Ying NI and Anatoliy MALYARENKO 10.1. Introduction 135 10.2. Asymptotic expansions of implied volatility 137 10.3. Performance of the asymptotic expansions 139 10.4. Calibration using the asymptotic expansions 141 10.4.1. A partial calibration procedure 142 10.4.2. Calibration to synthetic and market data 143 10.5. Conclusion and future work 147 10.6. References 148 Chapter 11. Performance Persistence of Polish Mutual Funds: Mobility Measures 149 Dariusz FILIP 11.1. Introduction 149 11.2. Literature review 150 11.3. Dataset and empirical design 153 11.4. Empirical results 155 11.5. Monthly perspective 156 11.6. Quarterly perspective 157 11.7. Yearly perspective 158 11.8. Conclusion 159 11.9. References 159 Chapter 12. Invariant Description for a Batch Version of the UCB Strategy with Unknown Control Horizon 163 Sergey GARBAR 12.1. Introduction 163 12.2. UCB strategy 165 12.3. Batch version of the strategy 165 12.4. Invariant description with a unit control horizon 166 12.5. Simulation results 169 12.6. Conclusion 170 12.7. Affiliations 171 12.8. References 171 Chapter 13. A New Non-monotonic Link Function for Beta Regressions 173 Gloria GHENO 13.1. Introduction 174 13.2. Model 175 13.3. Estimation 178 13.4. Comparison 179 13.5. Conclusion 184 13.6. References 184 Chapter 14. A Method of Big Data Collection and Normalizatio nfor Electronic Engineering Applications 187 Naveenbalaji GOWTHAMAN and Viranjay M. SRIVASTAVA 14.1. Introduction 187 14.2. Machine learning (ML) in electronic engineering 189 14.2.1. Data acquisition 190 14.2.2. Accessing the data repositories 191 14.2.3. Data storage and management 192 14.3. Electronic engineering applications – data science 193 14.4. Conclusion and future work 195 14.5. References 195 Chapter 15. Stochastic Runge–Kutta Solvers Based on Markov Jump Processes and Applications to Non-autonomous Systems of Differential Equations 199 Flavius GUIAŞ 15.1. Introduction 199 15.2. Description of the method 201 15.2.1. The direct simulation method 201 15.2.2. Picard iterations 201 15.2.3. Runge–Kutta steps 202 15.3. Numerical examples 203 15.3.1. The Lorenz system 203 15.3.2. A combustion model 204 15.4. Conclusion 206 15.5. References 206 Chapter 16. Interpreting a Topological Measure of Complexity for Decision Boundaries 207 Alan HYLTON, Ian LIM, Michael MOY and Robert SHORT 16.1. Introduction 207 16.2. Persistent homology 209 16.3. Methodology 213 16.3.1. Neural networks and binary classification 213 16.3.2. Persistent homology of a decision boundary 213 16.3.3. Procedure 214 16.4. Experiments and results 215 16.4.1. Three-dimensional binary classification 215 16.4.2. Data divided by a hyperplane 217 16.5. Conclusion and discussion 219 16.6. References 220 Chapter 17. The Minimum Renyi’s Pseudodistance Estimators for Generalized Linear Models 223 María JAENADA and Leandro PARDO 17.1. Introduction 223 17.2. The minimum RP estimators for the GLM model: asymptotic distribution 225 17.3. Example: Poisson regression model 230 17.3.1. Real data application 230 17.4. Conclusion 232 17.5. Acknowledgments 232 17.6. Appendix 232 17.6.1. Proof of Theorem 1 232 17.7. References 234 Chapter 18. Data Analysis based on Entropies and Measures of Divergence 237 Christos MESELIDIS, Alex KARAGRIGORIOU and Takis PAPAIOANNOU 18.1. Introduction 237 18.2. Divergence measures 238 18.3. Tests of fit based on Φ−divergence measures 241 18.4. Simulations 246 18.5. References 254 Part 3 259 Chapter 19. Geographically Weighted Regression for Official Land Prices and their Temporal Variation in Tokyo 261 Yuta KANNO and Takayuki SHIOHAMA 19.1. Introduction 261 19.2. Models and methodology 263 19.3. Data analysis 266 19.3.1. Data 266 19.3.2. Results 268 19.4. Conclusion 272 19.5. Acknowledgments 273 19.6. References 273 Chapter 20. Software Cost Estimation Using Machine Learning Algorithms 275 Sukran EBREN KARA and Rüya ŞAMLI 20.1. Introduction 275 20.2. Methodology 276 20.2.1. Dataset 276 20.2.2. Model 277 20.2.3. Evaluating the performance of the model 278 20.3. Results and discussion 279 20.4. Conclusion 282 20.5. References 283 Chapter 21. Monte Carlo Accuracy Evaluation of Laser Cutting Machine 285 Samuel KOSOLAPOV 21.1. Introduction 286 21.2. Mathematical model of a pintograph 286 21.3. Monte Carlo simulator 291 21.4. Simulation results 294 21.5. Conclusion 295 21.6. Acknowledgments 295 21.7. References 295 Chapter 22. Using Parameters of Piecewise Approximation by Exponents for Epidemiological Time Series Data Analysis 297 Samuel KOSOLAPOV 22.1. Introduction 298 22.2. Deriving equations for moving exponent parameters 298 22.3. Validation of derived equations by using synthetic data 300 22.4. Using derived equations to analyze real-life Covid-19 data 302 22.5. Conclusion 305 22.6. References 306 Chapter 23. The Correlation Between Oxygen Consumption and Excretion of Carbon Dioxide in the Human Respiratory Cycle 307 Anatoly KOVALENKO, Konstantin LEBEDINSKII and Verangelina MOLOSHNEVA 23.1. Introduction 308 23.2. Respiratory function physiology: ventilation–perfusion ratio 309 23.3. The basic principle of operation of artificial lung ventilation devices: patient monitoring parameters 310 23.4. The algorithm for monitoring the carbon emissions and oxygen consumption 312 23.5. Results 314 23.6. Conclusion 316 23.7. References 316 Part 4 317 Chapter 24. Approximate Bayesian Inference Using the Mean-Field Distribution 319 Antonin DELLA NOCE and Paul-Henry COURNÈDE 24.1. Introduction 319 24.2. Inference problem in a symmetric population system 321 24.2.1. Example of a symmetric system describing plant competition 321 24.2.2. Inference problem of the Schneider system, in a more general setting 323 24.3. Properties of the mean-field distribution 325 24.4. Mean-field approximated inference 327 24.4.1. Case of systems admitting a mean-field limit 327 24.5. Conclusion 330 24.6. References 330 Chapter 25. Pricing Financial Derivatives in the Hull–White Model Using Cubature Methods on Wiener Space 333 Hossein NOHROUZIAN, Anatoliy MALYARENKO and Ying NI 25.1. Introduction and outline 333 25.2. Cubature formulae on Wiener space 335 25.2.1. A simple example of classical Monte Carlo estimates 335 25.2.2. Modern Monte Carlo estimates via cubature method 336 25.2.3. An application in the Black–Scholes SDE 338 25.2.4. Trajectories of the cubature formula of degree 5 on Wiener space 339 25.2.5. Trajectories of price process given in equation [25.7] 340 25.2.6. An application on path-dependent derivatives 341 25.2.7. Trinomial tree (model) via cubature formulae of degree 5 342 25.3. Interest-rate models and Hull–White one-factor model 343 25.3.1. Equilibrium models 343 25.3.2. No-arbitrage models 344 25.3.3. Forward rate models 345 25.3.4. Hull–White one-factor model 345 25.3.5. Discretization of the Hull–White model via Euler scheme 346 25.3.6. Hull–White model for bond prices 346 25.4. The Hull–White model via cubature method 349 25.4.1. Simulating SDE [25.15] and ODE [25.24] 350 25.4.2. The Hull–White interest-rate tree via iterated cubature formulae: some examples 353 25.5. Discussion and future works 354 25.6. References 355 Chapter 26. Differences in the Structure of Infectious Morbidity of the Population during the First and Second Half of 2020 in St. Petersburg 359 Vasilii OREL, Olga NOSYREVA, Tatiana BULDAKOVA, Natalya GUREVA, Viktoria SMIRNOVA, Andrey KIM and Lubov SHARAFUTDINOVA 26.1. Introduction 360 26.2. Materials and methods 360 26.2.1. Characteristics of the territory of the district 360 26.2.2. Demographic characteristics of the area 360 26.2.3. Characteristics of the district medical service 361 26.2.4. The procedure for collecting primary information on cases of diseases of the population with a new coronavirus infection 361 26.3. Results of the analysis of the incidence of acute respiratory viral infectious diseases, new coronavirus infection Covid-19 and community-acquired pneumonia 362 26.4. Conclusion 367 26.5. References 368 Chapter 27. High Speed and Secured Network Connectivity for Higher Education Institutions Using Software Defined Networks 371 Lincoln S. PETER and Viranjay M. SRIVASTAVA 27.1. Introduction 372 27.2. Existing model review 373 27.3. Selection of a suitable model 374 27.4. Conclusion and future recommendations 376 27.5. References 376 Chapter 28. Reliability of a Double Redundant System Under the Full Repair Scenario 379 Vladimir RYKOV and Nika IVANOVA 28.1. Introduction 379 28.2. Problem statement, assumptions and notations 381 28.3. Reliability function 384 28.4. Time-dependent system state probabilities 386 28.4.1. General representation of t.d.s.p.s 386 28.4.2. T.d.s.p.s in a separate regeneration period 387 28.5. Steady-state probabilities 392 28.6. Conclusion 393 28.7. References 393 Chapter 29. Predicting Changes in Depression Levels Following the European Economic Downturn of 2008 395 Eleni SERAFETINIDOU and Georgia VERROPOULOU 29.1. Introduction 396 29.1.1. Aims of the study 398 29.2. Data and methods 398 29.2.1. Sample 398 29.2.2. Measures 398 29.3. Results 400 29.3.1. Descriptive findings 400 29.3.2. Non-respondents compared to respondents at baseline (wave 2) 403 29.3.3. Descriptive findings for respondents – analysis by gender 405 29.3.4. Findings regarding decreasing depression levels – analysis for the total sample and by gender 408 29.3.5. Findings regarding increasing depression levels – analysis for the total sample and by gender 410 29.4. Discussion 413 29.5. Conclusion 414 29.6. Acknowledgments 415 29.7. References 415 List of Authors 419 Index 425 Summary of Volume 2 429