Details
Applied Modeling Techniques and Data Analysis 2
Financial, Demographic, Stochastic and Statistical Models and Methods1. Aufl.
139,99 € |
|
Verlag: | Wiley |
Format: | |
Veröffentl.: | 26.03.2021 |
ISBN/EAN: | 9781119821632 |
Sprache: | englisch |
Anzahl Seiten: | 288 |
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Beschreibungen
<P><b>BIG DATA, ARTIFICIAL INTELLIGENCE AND DATA ANALYSIS SET Coordinated by Jacques Janssen</b> <p>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. <p>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.
<p>Preface xi<br /><i>Yannis DIMOTIKALIS, Alex KARAGRIGORIOU, Christina PARPOULA and Christos H. SKIADAS</i></p> <p><b>Part 1. Financial and Demographic Modeling Techniques </b><b>1</b></p> <p><b>Chapter 1. Data Mining Application Issues in the Taxpayer Selection Process </b><b>3<br /></b><i>Mauro BARONE, Stefano PISANI and Andrea SPINGOLA</i></p> <p>1.1. Introduction 3</p> <p>1.2. Materials and methods 5</p> <p>1.2.1. Data 5</p> <p>1.2.2. Interesting taxpayers 6</p> <p>1.2.3. Enforced tax recovery proceedings 9</p> <p>1.2.4. The models 11</p> <p>1.3. Results 13</p> <p>1.4. Discussion 23</p> <p>1.5. Conclusion 23</p> <p>1.6. References 24</p> <p><b>Chapter 2. Asymptotics of Implied Volatility in the Gatheral Double Stochastic Volatility Model </b><b>27<br /></b><i>Mohammed ALBUHAYRI, Anatoliy MALYARENKO, Sergei SILVESTROV, Ying NI, Christopher ENGSTRÖM, Finnan TEWOLDE and Jiahui ZHANG</i></p> <p>2.1. Introduction 27</p> <p>2.2. The results 30</p> <p>2.3. Proofs 30</p> <p>2.4. References 38</p> <p><b>Chapter 3. New Dividend Strategies </b><b>39<br /></b><i>Ekaterina BULINSKAYA</i></p> <p>3.1. Introduction 39</p> <p>3.2. Model 1 41</p> <p>3.3. Model 2 48</p> <p>3.4. Conclusion and further results 51</p> <p>3.5. Acknowledgments 51</p> <p>3.6. References 52</p> <p><b>Chapter 4. Introduction of Reserves in Self-adjusting Steering of Parameters of a Pay-As-You-Go Pension Plan </b><b>53<br /></b><i>Keivan DIAKITE, Abderrahim OULIDI and Pierre DEVOLDER</i></p> <p>4.1. Introduction 53</p> <p>4.2. The pension system 54</p> <p>4.3. Theoretical framework of the Musgrave rule 57</p> <p>4.4. Transformation of the retirement fund 60</p> <p>4.5. Conclusion 63</p> <p>4.6. References 64</p> <p><b>Chapter 5. Forecasting Stochastic Volatility for Exchange Rates using EWMA </b><b>65<br /></b><i>Jean-Paul MURARA, Anatoliy MALYARENKO, Milica RANCIC and Sergei SILVESTROV</i></p> <p>5.1. Introduction 65</p> <p>5.2. Data 66</p> <p>5.3. Empirical model 67</p> <p>5.4. Exchange rate volatility forecasting 69</p> <p>5.5. Conclusion 73</p> <p>5.6. Acknowledgments 73</p> <p>5.7. References 74</p> <p><b>Chapter 6. An Arbitrage-free Large Market Model for Forward Spread Curves </b><b>75<br /></b><i>Hossein NOHROUZIAN, Ying NI and Anatoliy MALYARENKO</i></p> <p>6.1. Introduction and background 75</p> <p>6.1.1. Term-structure (interest rate) models 76</p> <p>6.1.2. Forward-rate models versus spot-rate models 77</p> <p>6.1.3. The Heath–Jarrow–Morton framework 77</p> <p>6.1.4. Construction of our model 78</p> <p>6.2. Construction of a market with infinitely many assets 79</p> <p>6.2.1. The Cuchiero–Klein–Teichmann approach 79</p> <p>6.2.2. Adapting Cuchiero–Klein–Teichmann’s results to our objective 82</p> <p>6.3. Existence, uniqueness and non-negativity 82</p> <p>6.3.1. Existence and uniqueness: mild solutions 83</p> <p>6.3.2. Non-negativity of solutions 85</p> <p>6.4. Conclusion and future works 87</p> <p>6.5. References 88</p> <p><b>Chapter 7. Estimating the Healthy Life Expectancy (HLE) in the Far Past: The Case of Sweden (1751–2016) with Forecasts to 2060 </b><b>91<br /></b><i>Christos H. SKIADAS and Charilaos SKIADAS</i></p> <p>7.1. Life expectancy and healthy life expectancy estimates 92</p> <p>7.2. The logistic model 94</p> <p>7.3. The HALE estimates and our direct calculations 95</p> <p>7.4. Conclusion 96</p> <p>7.5. References 96</p> <p><b>Chapter 8. Vaccination Coverage Against Seasonal Influenza of Workers in the Primary Health Care Units in the Prefecture of Chania </b><b>97</b></p> <p><i>Aggeliki MARAGKAKI and George MATALLIOTAKIS</i></p> <p>8.1. Introduction 98</p> <p>8.2. Material and method 98</p> <p>8.3. Results 101</p> <p>8.4. Discussion 105</p> <p>8.5. References 107</p> <p><b>Chapter 9. Some Remarks on the Coronavirus Pandemic in Europe </b><b>109<br /></b><i>Konstantinos ZAFEIRIS and Marianna KOUKLI</i></p> <p>9.1. Introduction 109</p> <p>9.2. Background 110</p> <p>9.2.1. CoV pathogens 110</p> <p>9.2.2. Clinical characteristics of COVID-19 111</p> <p>9.2.3. Diagnosis 113</p> <p>9.2.4. Epidemiology and transmission of COVID-19 113</p> <p>9.2.5. Country response measures 115</p> <p>9.2.6. The role of statistical research in the case of COVID-19 and its challenges 119</p> <p>9.3. Materials and analyses 119</p> <p>9.4. The first phase of the pandemic 121</p> <p>9.5. Concluding remarks 126</p> <p>9.6. References 127</p> <p><b>Part 2. Applied Stochastic and Statistical Models and Methods </b><b>135</b></p> <p><b>Chapter 10. The Double Flexible Dirichlet: A Structured Mixture Model for Compositional Data </b><b>137<br /></b><i>Roberto ASCARI, Sonia MIGLIORATI and Andrea ONGARO</i></p> <p>10.1. Introduction 138</p> <p>10.1.1. The flexible Dirichlet distribution 139</p> <p>10.2. The double flexible Dirichlet distribution 140</p> <p>10.2.1. Mixture components and cluster means 141</p> <p>10.3. Computational and estimation issues 144</p> <p>10.3.1. Parameter estimation: the EM algorithm 145</p> <p>10.3.2. Simulation study 148</p> <p>10.4. References 151</p> <p><b>Chapter 11. Quantization of Transformed Lévy Measures </b><b>153<br /></b><i>Mark Anthony CARUANA</i></p> <p>11.1. Introduction 153</p> <p>11.2. Estimation strategy 156</p> <p>11.3. Estimation of masses and the atoms 159</p> <p>11.4. Simulation results 165</p> <p>11.5. Conclusion 166</p> <p>11.6. References 167</p> <p><b>Chapter 12. A Flexible Mixture Regression Model for Bounded Multivariate Responses </b><b>169<br /></b><i>Agnese M. DI BRISCO and Sonia MIGLIORATI</i></p> <p>12.1. Introduction 169</p> <p>12.2. Flexible Dirichlet regression model 170</p> <p>12.3. Inferential issues 172</p> <p>12.4. Simulation studies 173</p> <p>12.4.1. Simulation study 1: presence of outliers 174</p> <p>12.4.2. Simulation study 2: generic mixture of two Dirichlet distributions 179</p> <p>12.4.3. Simulation study3: FD distribution 180</p> <p>12.5. Discussion 182</p> <p>12.6. References 183</p> <p><b>Chapter 13. On Asymptotic Structure of the Critical Galton–Watson Branching Processes with Infinite Variance and Allowing Immigration </b><b>185<br /></b><i>Azam A. IMOMOV and Erkin E. TUKHTAEV</i></p> <p>13.1. Introduction 185</p> <p>13.2. Invariant measures of GW process 187</p> <p>13.3. Invariant measures of GWPI 190</p> <p>13.4. Conclusion 193</p> <p>13.5. References 194</p> <p><b>Chapter 14. Properties of the Extreme Points of the Joint Eigenvalue Probability Density Function of the Wishart Matrix </b><b>195<br /></b><i>Asaph Keikara MUHUMUZA, Karl LUNDENGÅRD, Sergei SILVESTROV, John Magero MANGO and Godwin KAKUBA</i></p> <p>14.1. Introduction 195</p> <p>14.2. Background 196</p> <p>14.3. Polynomial factorization of the Vandermonde and Wishart matrices 197</p> <p>14.4. Matrix norm of the Vandermonde and Wishart matrices 200</p> <p>14.5. Condition number of the Vandermonde and Wishart matrices 203</p> <p>14.6. Conclusion 206</p> <p>14.7. Acknowledgments 206</p> <p>14.8. References 207</p> <p><b>Chapter 15. Forecast Uncertainty of the Weighted TAR Predictor </b><b>211<br /></b><i>Francesco GIORDANO and Marcella NIGLIO</i></p> <p>15.1. Introduction 211</p> <p>15.2. SETAR predictors and bootstrap prediction intervals 214</p> <p>15.3. Monte Carlo simulation 218</p> <p>15.4. References 222</p> <p><b>Chapter 16. Revisiting Transitions Between Superstatistics </b><b>223<br /></b><i>Petr JIZBA and Martin PROKŠ</i></p> <p>16.1. Introduction 223</p> <p>16.2. From superstatistic to transition between superstatistics 224</p> <p>16.3. Transition confirmation 225</p> <p>16.4. Beck’s transition model 227</p> <p>16.5. Conclusion 230</p> <p>16.6. Acknowledgments 231</p> <p>16.7. References 231</p> <p><b>Chapter 17. Research on Retrial Queue with Two-Way Communication in a Diffusion Environment </b><b>233<br /></b><i>Viacheslav VAVILOV</i></p> <p>17.1. Introduction 233</p> <p>17.2. Mathematical model 234</p> <p>17.3. Asymptotic average characteristics 236</p> <p>17.4. Deviation of the number of applications in the system 241</p> <p>17.5. Probability distribution density of device states 247</p> <p>17.6. Conclusion 248</p> <p>17.7. References 248</p> <p>List of Authors 251</p> <p>Index 255</p>
<p><b>Yannis Dimotikalis</b> is Assistant Professor within the Department of Management Science and Technology at the Hellenic Mediterranean University, Greece. <p><b>Alex Karagrigoriou</b> 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. <p><b>Christina Parpoula</b> is Assistant Professor of Applied Statistics and Research Methodology within the Department of Psychology at the Panteion University of Social and Political Sciences, Greece. <p><b>Christos H. Skiadas</b> 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.
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