Data Analysis and Related Applications, Volume 5

Yiannis Dimotikalis works in the Department of Management Science and Technology at the Hellenic Mediterranean University, Greece. His research areas include teaching operations research (analytics), focusing on simulation and optimization.Christos H. Skiadas is the Founder and Director of the Data Analysis and Forecasting Laboratory in the Technical University of Crete, Greece. He is also the former Vice-Rector of the Technical University of Crete and Chairman of the Department of Production Engineering and Management.

• Chapter 1 Modeling/Forecasting Patient Recruitment in Multicenter Clinical Trials Using Time-dependent Models 1Volodymyr ANISIMOV and Lucas OLIVER1.1 Introduction 11.2 Poisson-gamma model with time-dependent rates 51.2.1 The case of homogeneous rates 51.3 Non-homogeneous PG model 71.3.1 Estimation at the interim stage 91.3.2 Simulation of non-homogeneous PG model 101.4 Testing the recruitment rates for homogeneity 111.4.1 Poisson-type test 121.4.2 Criterion for testing hypothesis H 0 131.4.3 Poisson-gamma test 151.5 Implementations 191.6 Acknowledgment 201.7 References 20Chapter 2 Forecasting the Next Megacycle of the Economy 23George S. ATSALAKIS and Ioanna ATSALAKI2.1 Introduction 232.2 2024: the end of an economic megacycle 242.3 The role of technology in shaping the future 252.4 The economic consequences of the new cycle 252.4.1 Presenting past megacycles 262.4.2 The structure of economic megacycles 262.4.3 Technology as a catalyst for megacycles 272.4.4 Historical patterns of energy and economic growth 272.5 The future of economic megacycles 282.5.1 The 2024–2080 megacycle: a new era of exponential change and growth 282.5.2 Stagnation phase: 2024–2052 292.5.3 Growth phase: 2052–2080 292.5.4 Geopolitical and societal implications 302.5.5 Role of labor and automation 312.6 Conclusions 312.7 References 32Chapter 3 Modeling Functioning as a Determinant of Wellbeing: A Mediation Analysis 35Anastasia CHARALAMPI and Catherine MICHALOPOULOU3.1 Introduction 353.2 Methods 373.2.1 Procedure and participants 373.2.2 Measures and item selection 383.2.3 Statistical analyses 403.3 Results 413.3.1 Univariate analysis 413.3.2 Bivariate analysis: correlation analysis 423.3.3 Multivariate analysis: mediation analysis 423.4 Conclusions 473.5 References 483.6 Appendix 51Chapter 4 Cross-Cultural Issues in Psychological Assessment: A Multistrategy Approach 53Franca CRIPPA, Giulia GOTTI, Raffaella CALATI, Mariangela ZENGA, Kainaat DANYAL and Naved IQBAL4.1 Introduction 534.2 Suicide risk among university students: India and Italy in direct comparison 544.3 Merging techniques: A better way in cross-cultural studies? 564.4 Do different people respond in the same way to common items? 584.5 Conclusions 614.6 References 61Chapter 5 A Control Chart for Zero-Inflated Semi-Continuous Data 65Fernanda Otília FIGUEIREDO, Adelaide FIGUEIREDO and M. Ivette GOMES5.1 Introduction 655.1.1 Zero-inflated and hurdle models for count data 665.1.2 Inflated distributions for semi-continuous data 665.2 Zero-inflated Lomax distribution 675.2.1 The Lomax and the zero-inflated Lomax distributions 675.2.2 Maximum likelihood estimates 685.3 Shewhart control chart for monitoring zero-inflated Lomax data 695.4 Performance of the proposed control chart 705.5 Conclusions 745.6 Acknowledgments 765.7 References 76Chapter 6 Further Results on Location Invariant Estimation of the Weibull Tail Coefficient 79M. Ivette GOMES, Frederico CAEIRO and Lígia HENRIQUES-RODRIGUES6.1 Introduction 796.2 Hill and GMs EVI and WTC-estimators 816.2.1 Power-mean-of-exponent- p (PM p) and Holder’s mean-of-order-p (MO p ≡ H p) EVI estimation 816.2.2 WTC estimation 836.3 Classes of PORT-GMs (PGMs) WTC-estimators 846.4 Monte Carlo simulation of the PORT-GPM p (PGPM p) WTC-estimators 856.5 Overall comments and open research topics 896.6 Acknowledgments 916.7 References 91Chapter 7 What Can we Learn from Malta? An Exploration of Gender Disparities in Education, Work and Money in Europe 95Erika GRAMMATICA, Francesca GRESELIN and Mariangela ZENGA7.1 Introduction 957.2 Gender gap: education, work and money 967.3 Gender Equality Index 987.4 Three-way data approach based on principal component analysis 1007.5 Evidence from principal component analysis 1027.6 Results of trajectory analysis 1037.7 Conclusions 1067.8 Acknowledgments 1067.9 References 107Chapter 8 Financial Analysis of a Public Hospital: The Case of the Corfu General Hospital 109Margarita IOANNIDOU and George MATALLIOTAKIS8.1 Introduction 1098.2 Materials and methods 1108.3 Results 1108.3.1 Liquidity ratios 1108.3.2 Financial structure and viability ratios 1118.3.3 Activity ratios 1128.3.4 Profitability ratios 1138.4 Discussion 1148.5 References 115Chapter 9 EWMA Control Charts for Skewed Distributions 117Derya KARAGÖZ and Moustapha Aminou TUKUR9.1 Introduction 1179.2 Exponentially weighted moving average control charts 1199.3 EMMA control charts for the non-normal process 1209.3.1 The WV EWMA control chart 1219.3.2 The WSD EWMA control chart 1219.3.3 Newly proposed SC EWMA control chart 1229.4 Real data 1229.5 Simulation study 1259.6 Simulation algorithm 1269.7 Results and discussion 1279.8 Conclusion 1319.9 References 133Chapter 10 Assessing the Impact of Renewable Energy Sources on Energy Economics: A Non-Linear Regression Analysis of Hellenic Energy Exchange Market Clearing Prices 135Emmanuel KARAPIDAKIS, Yiannis KATSIGIANNIS, Konstantinos BLAZAKIS, Marios NIKOLOGIANNIS, George MATALLIOTAKIS, Georgios STAVRAKAKIS, Nikos VENIANAKIS and Paolo BONFINI10.1 Introduction 13510.2 Methodology 13710.2.1 Spearman’s rank 13710.2.2 Sparse autoencoder 13910.3 Results 14010.4 Discussion 14210.5 Conclusions 14310.6 Acknowledgments 14310.7 References 144Chapter 11 Enhancing Energy Market Stability: Comparative Analysis of Forecasting Techniques for Market Clearing Prices in the Day-Ahead Market 147Emmanuel KARAPIDAKIS, Yiannis KATSIGIANNIS, Konstantinos BLAZAKIS, Marios NIKOLOGIANNIS, George MATALLIOTAKIS, Georgios STAVRAKAKIS, Nikos VENIANAKIS and Nikolaos SCHETAKIS11.1 Introduction 14711.2 Methodology 15011.3 Results 15211.4 Discussion 15511.5 Conclusions 15511.6 Acknowledgments 15511.7 References 156Chapter 12 Using the Coxian Continuous-Time Hidden Markov Model to Analyze Lombardy Region Wards for Older Individuals 159Hannah MITCHELL, Adele H. MARSHALL and Mariangela ZENGA12.1 Introduction 16012.2 Methodology 16112.3 Data and results 16512.3.1 Data 16512.3.2 Results 16512.4 Conclusions 17212.5 Practice implications 17212.6 Conflict of interest 17312.7 References 173Chapter 13 Estimators for Extreme Value Index: Advancements in Tail Inference 177Ayana MATEUS and Frederico CAEIRO13.1 Introduction 17713.2 Estimators for the tail parameters 17913.2.1 The new class of estimators for the EVI 17913.2.2 Asymptotic properties of the GPWM estimators 18113.2.3 Estimating an extreme quantile 18213.3 Monte Carlo simulation study of the GPWM estimators 18213.3.1 Methodology 18313.3.2 Results 18313.4 Conclusion 18513.5 Acknowledgments 18513.6 References 185Chapter 14 Determinants of Students’ Attitude Toward History: An Empirical Approach 187Aristea MAVROGIANNI, Eleni VASILAKI and Maria GRYDAKI14.1 Introduction 18814.2 Previous research 19014.2.1 Attitude toward history 19014.2.2 Educational factors 19114.2.3 Socioeconomic factors 19114.3 Data and methods 19414.3.1 Data 19414.3.2 Empirical methodology 19714.4 Results 19814.5 Summary and conclusions 20314.6 Appendices 20414.6.1 Appendix A: the initial full questionnaire for the attitude survey toward history (EDIS) 20414.6.2 Appendix B: the final questionnaire for the attitude survey toward history (EDIS) 20614.6.3 Appendix C 20714.7 References 208Chapter 15 Methodological Procedures for Assessing the Quality of Death Certificates Due to Unknown Causes 217Neir Antunes PAES15.1 Introduction 21715.2 Methods 21915.2.1 First step: correction of underregistration of deaths (f) 22015.2.2 Second step: redistribution of deaths due to ill-defined causes 22315.2.3 Third step: redistribution of deaths due to non-specific causes (garbage codes) 22615.3 Illustrative example 22715.4 Conclusions 23015.5 References 230Chapter 16 Health Status, Cancer and Pneumonia Death Rates in Europe: 2019–2022 233Elena ŘÍHOVÁ and Kornélia SVAČINOVÁ16.1 Introduction 23316.2 Background 23416.3 Methods 23516.4 Results and discussion 23716.4 Conclusions 24416.5 References 244Chapter 17 A Bayesian Asymmetric Approach to Modeling Volatility on Portfolios with Many Assets 247David SUDA, Monique Borg INGUANEZ and Matthew CAMILLERI17.1 Introduction 24717.2 Dynamic principal component analysis 24817.3 Bayesian Student-t GJR(1,1) model 25017.4 Asymmetric modeling of a portfolio with many assets 25117.5 Forecasting, predictive ability and risk estimation 25317.6 Conclusion 25617.7 References 256Chapter 18 Pandemic-Driven Innovations: Utilizing Online Learning and Big Data Analysis for Decision-Making in Educational Environments 259Leonidas THEODORAKOPOULOS, Ioanna KALLIAMPAKOU, Alexandra THEODOROPOULOU and Gerasimos KALOGERATOS18.1 Introduction 26018.2 Literature review 26018.2.1 Difficulties during the COVID-19 period 26018.2.2 Effects of COVID-19 on education 26118.2.3 Big data analysis in educational research 26218.2.4 Related work 26318.3 Methodology 26418.4 Research questions 26418.4.1 Dataset presentation 26518.5 Conclusion 27318.6 Suggestions for further research 27418.7 References 274Chapter 19 Credit Card Fraud Detection with Machine Learning and Big Data Analytics: A PySpark Framework Implementation 281Leonidas THEODORAKOPOULOS, Ioanna KALLIAMPAKOU, Alexandra THEODOROPOULOU and Fotini ZAKKA19.1 Introduction 28119.2 Literature review 28319.2.1 Introduction to credit card fraud detection 28319.2.2 The importance of detecting credit card fraud 28419.2.3 Role of machine learning in improving decision-making processes in fraud detection 28419.2.4 Automated pattern recognition 28519.2.5 Predictive modeling 28519.2.6 Dynamic risk scoring 28519.2.7 Anomaly detection 28619.2.8 Natural language processing (NLP) 28619.2.9 Integration with existing systems 28619.2.10 Credit card fraud detection: machine learning applications 28619.2.11 Credit card fraud detection using Apache Spark 28719.2.12 How can machine learning algorithms enhance decision quality in detecting fraud? 28819.2.13 Improved detection accuracy 28819.2.14 Real-time processing and analysis 28819.2.15 Handling big data and complex variables 28919.2.16 Adaptive learning for evolving threats 28919.2.17 Cost efficiency through automation 28919.2.18 Enhanced scalability 28919.3 Materials and methods 29019.3.1 Performance evaluation 29219.4 Results 30719.4.1 Comparative analysis 31419.5 Conclusions 31519.6 Future work 31719.7 Limitations 31719.8 References 318Chapter 20 Quantitative Modeling of the Demographic Aging Process 323Grażyna TRZPIOT20.1 Introduction 32320.2 Literature review 32420.3 Methodology 32520.3.1 Dependency ratios – double aging index 32720.3.2 Multivariate regression model for the double aging index 33020.4 Research results and discussion 33020.5 Conclusions 33120.6 References 332Chapter 21 Hotel Sales During COVID-19: Evidence from the United States 333Dimitrios VORTELINOS, Christos FLOROS, Alexandros APOSTOLAKIS and Ioannis PASSAS21.1 Introduction 33321.2 Literature review 33421.3 Travel and tourism economic impact in 2022 33521.3.1 Travel and tourism GDP 33521.3.2 Travel and tourism employment 33521.3.3 Travel and tourism forecasts 33621.4 Key developments in the hospitality sector in 2022 33621.4.1 Overall highlights 33621.4.2 Hotel room demand 33721.4.3 Occupancy 33721.4.4 Room revenue 33821.4.5 Workforce 33821.4.6 State and local tax revenue 33821.4.7 US hotel markets 33921.5 Data and methodology 34021.5.1 Data description 34021.5.2 Methodology 34021.5.3 LARC score 34021.5.4 Graphical analysis of variables 34121.6 Results 34321.7 Concluding remarks and future research 34621.8 Acknowledgments 34621.9 References 346Chapter 22 Investigating the Mediating Role of Religious Services Attendance in the Relationship Between Religion Variables and Social Class Perceptions 349Aggeliki YFANTI and Catherine MICHALOPOULOU22.1 Introduction 34922.2 Method 35122.2.1 Procedure and participants 35122.2.2 Measures 35222.2.3 Statistical analyses 35322.3 Results 35522.3.1 Univariate analyses 35522.3.2 Bivariate analyses: correlation analyses 35922.3.3 Multivariate analyses: mediation analyses 36022.4 Conclusions 36222.5 References 363Chapter 23 New Methods of Constructing Confidence Intervals of a Sensitive Proportion in Survey Statistics 365Marta ZALEWSKA and Wojciech NIEMIRO23.1 Introduction 36523.2 Method of moments (MM) for ICT data 36723.3 EM estimation for ICT data and parametric bootstrap 36723.3.1 Maximum likelihood via the EM algorithm 36823.3.2 Percentile parametric bootstrap confidence intervals 36923.4 “Almost exact” confidence intervals for ICT data 36923.5 Simulation results 37123.6 Conclusions 37523.7 References 375Chapter 24 Spreading Diseases Models Under Vaccination 377S. ZIMERAS and D. VASILEIOU24.1 Introduction 37724.2 Modeling epidemic disease 37824.3 Vaccination models analysis 38024.4 SEIVR model 38024.5 SIRV model 38224.6 Conclusions 38424.7 References 385List of Authors 387Index 393