Financial, demographic, stochastic and statistical models and methods
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書誌事項
Financial, demographic, stochastic and statistical models and methods
（Innovation, entrepreneurship, management series, . Big data,
ISTE , Wiley, 2021
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
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.
目次
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 ENGSTROEM, 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 Levy 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
Asaph Keikara MUHUMUZA, Karl LUNDENGARD, Sergei SILVESTROV, John Magero MANGO and Godwin KAKUBA
14.1. Introduction 195
14.2. Background 196
14.3. Polynomial factorization of the Vandermonde and Wishart matrices 197
14.4. Matrix norm of the Vandermonde and Wishart matrices 200
14.5. Condition number of the Vandermonde and Wishart matrices 203
14.6. Conclusion 206
14.7. Acknowledgments 206
14.8. References 207
Chapter 15. Forecast Uncertainty of the Weighted TAR Predictor 211
Francesco GIORDANO and Marcella NIGLIO
15.1. Introduction 211
15.2. SETAR predictors and bootstrap prediction intervals 214
15.3. Monte Carlo simulation 218
15.4. References 222
Chapter 16. Revisiting Transitions Between Superstatistics 223
Petr JIZBA and Martin PROKS
16.1. Introduction 223
16.2. From superstatistic to transition between superstatistics 224
16.3. Transition confirmation 225
16.4. Beck's transition model 227
16.5. Conclusion 230
16.6. Acknowledgments 231
16.7. References 231
Chapter 17. Research on Retrial Queue with Two-Way Communication in a Diffusion Environment 233
Viacheslav VAVILOV
17.1. Introduction 233
17.2. Mathematical model 234
17.3. Asymptotic average characteristics 236
17.4. Deviation of the number of applications in the system 241
17.5. Probability distribution density of device states 247
17.6. Conclusion 248
17.7. References 248
List of Authors 251
Index 255
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