Applied Modeling Techniques and Data Analysis 2

BIG DATA, ARTIFICIAL INTELLIGENCE AND DATA ANALYSIS SET Coordinated by Jacques Janssen

Data analysis is a scientific field that continues to grow enormously, most notably over the last few decades, following rapid growth within the tech industry, as well as the wide applicability of computational techniques alongside new advances in analytic tools. Modeling enables data analysts to identify relationships, make predictions, and to understand, interpret and visualize the extracted information more strategically.

This book includes the most recent advances on this topic, meeting increasing demand from wide circles of the scientific community. Applied Modeling Techniques and Data Analysis 2 is a collective work by a number of leading scientists, analysts, engineers, mathematicians and statisticians, working on the front end of data analysis and modeling applications. The chapters cover a cross section of current concerns and research interests in the above scientific areas. The collected material is divided into appropriate sections to provide the reader with both theoretical and applied information on data analysis methods, models and techniques, along with appropriate applications.

Auteur

Yannis Dimotikalis is Assistant Professor within the Department of Management Science and Technology at the Hellenic Mediterranean University, Greece. Alex Karagrigoriou is Professor of Probability and Statistics, Deputy Director of Graduate Studies in Statistics and Actuarial-Financial Mathematics, and Director of the Laboratory of Statistics and Data Analysis within the Department of Statistics and Actuarial-Financial Mathematics at the University of the Aegean, Greece. Christina Parpoula is Assistant Professor of Applied Statistics and Research Methodology within the Department of Psychology at the Panteion University of Social and Political Sciences, Greece. Christos H. Skiadas is Former Vice-Rector at the Technical University of Crete, Greece, and founder of its Data Analysis and Forecasting Laboratory. He continues his research in ManLab, within the faculty s Department of Production Engineering and Management.

Contenu

Preface xi
Yannis DIMOTIKALIS, Alex KARAGRIGORIOU, Christina PARPOULA and Christos H. SKIADAS

Part 1. Financial and Demographic Modeling Techniques 1

Chapter 1. Data Mining Application Issues in the Taxpayer Selection Process 3
*Mauro BARONE, Stefano PISANI and Andrea SPINGOLA*

1.1. Introduction 3

1.2. Materials and methods 5

1.2.1. Data 5

1.2.2. Interesting taxpayers 6

1.2.3. Enforced tax recovery proceedings 9

1.2.4. The models 11

1.3. Results 13

1.4. Discussion 23

1.5. Conclusion 23

1.6. References 24

Chapter 2. Asymptotics of Implied Volatility in the Gatheral Double Stochastic Volatility Model 27
*Mohammed ALBUHAYRI, Anatoliy MALYARENKO, Sergei SILVESTROV, Ying NI, Christopher ENGSTRÖM, Finnan TEWOLDE and Jiahui ZHANG*

2.1. Introduction 27

2.2. The results 30

2.3. Proofs 30

2.4. References 38

Chapter 3. New Dividend Strategies 39
*Ekaterina BULINSKAYA*

3.1. Introduction 39

3.2. Model 1 41

3.3. Model 2 48

3.4. Conclusion and further results 51

3.5. Acknowledgments 51

3.6. References 52

Chapter 4. Introduction of Reserves in Self-adjusting Steering of Parameters of a Pay-As-You-Go Pension Plan 53
*Keivan DIAKITE, Abderrahim OULIDI and Pierre DEVOLDER*

4.1. Introduction 53

4.2. The pension system 54

4.3. Theoretical framework of the Musgrave rule 57

4.4. Transformation of the retirement fund 60

4.5. Conclusion 63

4.6. References 64

Chapter 5. Forecasting Stochastic Volatility for Exchange Rates using EWMA 65
*Jean-Paul MURARA, Anatoliy MALYARENKO, Milica RANCIC and Sergei SILVESTROV*

5.1. Introduction 65

5.2. Data 66

5.3. Empirical model 67

5.4. Exchange rate volatility forecasting 69

5.5. Conclusion 73

5.6. Acknowledgments 73

5.7. References 74

Chapter 6. An Arbitrage-free Large Market Model for Forward Spread Curves 75
*Hossein NOHROUZIAN, Ying NI and Anatoliy MALYARENKO*

6.1. Introduction and background 75

6.1.1. Term-structure (interest rate) models 76

6.1.2. Forward-rate models versus spot-rate models 77

6.1.3. The Heath-Jarrow-Morton framework 77

6.1.4. Construction of our model 78

6.2. Construction of a market with infinitely many assets 79

6.2.1. The Cuchiero-Klein-Teichmann approach 79

6.2.2. Adapting Cuchiero-Klein-Teichmann's results to our objective 82

6.3. Existence, uniqueness and non-negativity 82

6.3.1. Existence and uniqueness: mild solutions 83

6.3.2. Non-negativity of solutions 85

6.4. Conclusion and future works 87

6.5. References 88

Chapter 7. Estimating the Healthy Life Expectancy (HLE) in the Far Past: The Case of Sweden (1751-2016) with Forecasts to 2060 91
*Christos H. SKIADAS and Charilaos SKIADAS*

7.1. Life expectancy and healthy life expectancy estimates 92

7.2. The logistic model 94

7.3. The HALE estimates and our direct calculations 95

7.4. Conclusion 96

7.5. References 96

Chapter 8. Vaccination Coverage Against Seasonal Influenza of Workers in the Primary Health Care Units in the Prefecture of Chania 97

Aggeliki MARAGKAKI and George MATALLIOTAKIS

8.1. Introduction 98

8.2. Material and method 98

8.3. Results 101

8.4. Discussion 105

8.5. References 107

Chapter 9. Some Remarks on the Coronavirus Pandemic in Europe 109
*Konstantinos ZAFEIRIS and Marianna KOUKLI*

9.1. Introduction 109

9.2. Background 110

9.2.1. CoV pathogens 110

9.2.2. Clinical characteristics of COVID-19 111

9.2.3. Diagnosis 113

9.2.4. Epidemiology and transmission of COVID-19 113

9.2.5. Country response measures 115

9.2.6. The role of statistical research in the case of COVID-19 and its challenges 119

9.3. Materials and analyses 119

9.4. The first phase of the pandemic 121

9.5. Concluding remarks 126

9.6. References 127

Part 2. Applied Stochastic and Statistical Models and Methods 135

Chapter 10. The Double Flexible Dirichlet: A Structured Mixture Model for Compositional Data 137
*Roberto ASCARI, Sonia MIGLIORATI and Andrea ONGARO*

10.1. Introduction 138

10.1.1. The flexible Dirichlet distribution 139

10.2. The double flexible Dirichlet distribution 140

10.2.1. Mixture components and cluster means 141

10.3. Computational and estimation issues 144

10.3.1. Parameter estimation: the EM algorithm 145

10.3.2. Simulation study 148

10.4. References 151

Chapter 11. Quantization of Transformed Lévy Measures 153
*Mark Anthony CARUANA*

11.1. Introduction 153

11.2. Estimation strategy 156

11.3. Estimation of masses and the atoms 159

11.4. Simulation results 165

11.5. Conclusion 166

11.6. References 167

Chapter 12. A Flexible Mixture Regression Model for Bounded Multivariate Responses 169
*Agnese M. DI BRISCO and Sonia MIGLIORATI*

12.1. Introduction 169

12.2. Flexible Dirichlet regression model 170

12.3. Inferential issues 172

12.4. Simulation studies 173

12.4.1. Simulation study 1: presence of outliers 174

12.4.2. Simulation study 2: generic mixture of two Dirichlet distributions 179

12.4.3. Simulation study3: FD distribution 180

12.5. Discussion 182

12.6. References 183

Chapter 13. On Asymptotic Structure of the Critical Galton-Watson Branching Processes with Infinite Variance and Allowing Immigration 185
*Azam A. IMOMOV and Erkin E. TUKHTAEV*

13.1. Introduction 185

13.2. Invariant measures of GW process 187

13.3. Invariant measures of GWPI 190

13.4. Conclusion 193

13.5. References 194

Chapter 14. Properties of the Extreme Points of the Joint Eigenvalue Probability Density Function of the Wishart Matrix **195 …