Applied Modeling Techniques and Data Analysis 1: Computational Data Analysis Methods and Tools

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 1 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.

Table of Contents:
Preface xi Yannis DIMOTIKALIS, Alex KARAGRIGORIOU, Christina PARPOULA and Christos H. SKIADAS Part 1. Computational Data Analysis 1 Chapter 1. A Variant of Updating PageRank in Evolving Tree Graphs 3 Benard ABOLA, Pitos Seleka BIGANDA, Christopher ENGSTRÖM, John Magero MANGO, Godwin KAKUBA and Sergei SILVESTROV 1.1. Introduction 3 1.2. Notations and definitions 5 1.3. Updating the transition matrix 5 1.4. Updating the PageRank of a tree graph 10 1.4.1. Updating the PageRank of tree graph when a batch of edges changes 12 1.4.2. An example of updating the PageRank of a tree 15 1.5. Maintaining the levels of vertices in a changing tree graph 17 1.6. Conclusion 21 1.7. Acknowledgments 21 1.8. References 21 Chapter 2. Nonlinearly Perturbed Markov Chains and Information Networks 23 Benard ABOLA, Pitos Seleka BIGANDA, Sergei SILVESTROV, Dmitrii SILVESTROV, Christopher ENGSTRÖM, John Magero MANGO and Godwin KAKUBA 2.1. Introduction 23 2.2. Stationary distributions for Markov chains with damping component 26 2.2.1. Stationary distributions for Markov chains with damping component 26 2.2.2. The stationary distribution of the Markov chain X0,n 28 2.3. A perturbation analysis for stationary distributions of Markov chains with damping component 29 2.3.1. Continuity property for stationary probabilities 29 2.3.2. Rate of convergence for stationary distributions 29 2.3.3. Asymptotic expansions for stationary distributions 30 2.3.4. Results of numerical experiments 32 2.4. Coupling and ergodic theorems for perturbed Markov chains with damping component 39 2.4.1. Coupling for regularly perturbed Markov chains with damping component 39 2.4.2. Coupling for singularly perturbed Markov chains with damping component 41 2.4.3. Ergodic theorems for perturbed Markov chains with damping component in the triangular array mode 42 2.4.4. Numerical examples 43 2.5. Acknowledgments 51 2.6. References 51 Chapter 3. PageRank and Perturbed Markov Chains 57 Pitos Seleka BIGANDA, Benard ABOLA, Christopher ENGSTRÖM, Sergei SILVESTROV, Godwin KAKUBA and John Magero MANGO 3.1. Introduction 57 3.2. PageRank of the first-order perturbed Markov chain 59 3.3. PageRank of the second-order perturbed Markov chain 60 3.4. Rates of convergence of Page Ranks of first- and second-order perturbed Markovchains 70 3.5. Conclusion 72 3.6. Acknowledgments 72 3.7. References 72 Chapter 4. Doubly Robust Data-driven Distributionally Robust Optimization 75 Jose BLANCHET, Yang KANG, Fan ZHANG, Fei HE and Zhangyi HU 4.1. Introduction 75 4.2. DD-DRO, optimal transport and supervised machine learning 79 4.2.1. Optimal transport distances and discrepancies 80 4.3. Data-driven selection of optimal transport cost function 81 4.3.1. Data-driven cost functions via metric learning procedures 81 4.4. Robust optimization for metric learning 83 4.4.1. Robust optimization for relative metric learning 83 4.4.2. Robust optimization for absolute metric learning 86 4.5. Numerical experiments 88 4.6. Discussion and conclusion 89 4.7. References 89 Chapter 5. A Comparison of Graph Centrality Measures Based on Lazy Random Walks 91 Collins ANGUZU, Christopher ENGSTRÖM and Sergei SILVESTROV 5.1. Introduction 91 5.1.1. Notations and abbreviations 93 5.1.2. Linear systems and the Neumann series 94 5.2. Review on some centrality measures 95 5.2.1. Degree centrality 95 5.2.2. Katz status and β-centralities 95 5.2.3. Eigenvector and cumulative nomination centralities 96 5.2.4. Alpha centrality 97 5.2.5. PageRank centrality 98 5.2.6. Summary of the centrality measures as steady state, shifted and power series 99 5.3. Generalizations of centrality measures 99 5.3.1. Priors to centrality measures 99 5.3.2. Lazy variants of centrality measures 100 5.3.3. Lazy α-centrality 100 5.3.4. Lazy Katz centrality 102 5.3.5. Lazy cumulative nomination centrality 103 5.4. Experimental results 104 5.5. Discussion 106 5.6. Conclusion 109 5.7. Acknowledgments 109 5.8. References 110 Chapter 6. Error Detection in Sequential Laser Sensor Input 113 Gwenael GATTO and Olympia HADJILIADIS 6.1. Introduction 113 6.2. Data description 114 6.3. Algorithms 116 6.3.1. Algorithm for consecutive changes in mean 118 6.3.2. Algorithm for burst detection 120 6.4. Results 125 6.5. Acknowledgments 127 6.6. References 127 Chapter 7. Diagnostics and Visualization of Point Process Models for Event Times on a Social Network 129 Jing WU, Anna L. SMITH and Tian ZHENG 7.1. Introduction 129 7.2. Background 131 7.2.1. Univariate point processes 131 7.2.2. Network point processes 132 7.3. Model checking for time heterogeneity 134 7.3.1. Time rescaling theorem 134 7.3.2. Residual process 136 7.4. Model checking for network heterogeneity and structure 138 7.4.1. Kolmogorov–Smirnov test 138 7.4.2. Structure score based on the Pearson residual matrix 141 7.5. Summary 143 7.6. Acknowledgments 144 7.7. References 144 Part 2. Data Analysis Methods and Tools 147 Chapter 8. Exploring the Distribution of Conditional Quantile Estimates: An Application to Specific Costs of Pig Production in the European Union 149 Dominique DESBOIS 8.1. Introduction 150 8.2. Conceptual framework and methodological aspects 150 8.2.1. The empirical model for estimating the specific production costs 151 8.2.2. The procedures for estimating and testing conditional quantiles 152 8.2.3. Symbolic PCA of the specific cost distributions 154 8.2.4. Symbolic clustering analysis of the specific cost distributions 162 8.3. Results 165 8.3.1. The SO-PCA of specific cost estimates 167 8.3.2. The divisive hierarchy of specific cost estimates 170 8.4. Conclusion 171 8.5. References 172 Chapter 9. Maximization Problem Subject to Constraint of Availability in Semi-Markov Model of Operation 175 Franciszek GRABSKI 9.1. Introduction 175 9.2. Semi-Markov decision process 176 9.3. Semi-Markov decision model of operation 177 9.3.1. Description and assumptions 177 9.3.2. Model construction 177 9.4. Optimization problem 178 9.4.1. Linear programming method 179 9.5. Numerical example 182 9.6. Conclusion 184 9.7. References 185 Chapter 10. The Impact of Multicollinearity on Big Data Multivariate Analysis Modeling 187 Kimon NTOTSIS and Alex KARAGRIGORIOU 10.1. Introduction 187 10.2. Multicollinearity 188 10.3. Dimension reduction techniques 191 10.3.1. Beale et al 192 10.3.2. Principal component analysis 192 10.4. Application 194 10.4.1. The modeling of PPE 194 10.4.2. Concluding remarks 200 10.5. Acknowledgments 200 10.6. References 200 Chapter 11. Weak Signals in High-Dimensional Poisson Regression Models 203 Orawan REANGSEPHET, Supranee LISAWADI and Syed Ejaz AHMED 11.1. Introduction 203 11.2. Statistical background 204 11.3. Methodologies 205 11.3.1. Predictor screening methods 205 11.3.2. Post-screening parameter estimation methods 206 11.4. Numerical studies 208 11.4.1. Simulation settings and performance criteria 208 11.4.2. Results 209 11.5. Conclusion 217 11.6. Acknowledgments 218 11.7. References 218 Chapter 12. Groundwater Level Forecasting for Water Resource Management 221 Andrea ZIRULIA, Alessio BARBAGLI and Enrico GUASTALDI 12.1. Introduction 221 12.2. Materials and methods 222 12.2.1. Study area 222 12.2.2. Forecast method 222 12.3. Results 224 12.4. Conclusion 230 12.5. References 230 Chapter 13. Phase I Non-parametric Control Charts for Individual Observations: A Selective Review and Some Results 233 Christina PARPOULA 13.1. Introduction 234 13.1.1. Background 234 13.1.2. Univariate non-parametric process monitoring 235 13.2. Problem formulation 237 13.3. A comparative study 239 13.3.1. The existing methodologies 239 13.3.2. Simulation settings 240 13.3.3. Simulation-study results 242 13.4. Concluding remarks 247 13.5. References 247 Chapter 14. On Divergence and Dissimilarity Measures for Multiple Time Series 249 Konstantinos MAKRIS, Alex KARAGRIGORIOU and Ilia VONTA 14.1. Introduction 249 14.2. Classical measures 250 14.3. Divergence measures 252 14.4. Dissimilarity measures for ordered data 254 14.4.1. Standard dissimilarity measures 254 14.4.2. Advanced dissimilarity measures 256 14.5. Conclusion 259 14.6. References 259 List of Authors 261 Index 265