Details

Autor(en) / Beteiligte

• Karagrigoriou, Alex,

Titel

Applied modeling techniques and data analysis. 1. Computational data analysis methods and tools

Ort / Verlag

London, England ; : ISTE

Erscheinungsjahr

[2021]

Beschreibungen/Notizen

• Cover -- Half-Title Page -- Title Page -- Copyright Page -- Contents -- Preface -- PART 1: Computational Data Analysis -- 1 A Variant of Updating PageRank in Evolving Tree Graphs -- 1.1. Introduction -- 1.2. Notations and definitions -- 1.3. Updating the transition matrix -- 1.4. Updating the PageRank of a tree graph -- 1.4.1. Updating the PageRank of tree graph when a batch of edges changes -- 1.4.2. An example of updating the PageRank of a tree -- 1.5. Maintaining the levels of vertices in a changing tree graph -- 1.6. Conclusion -- 1.7. Acknowledgments -- 1.8. References -- 2 Nonlinearly Perturbed Markov Chains and Information Networks -- 2.1. Introduction -- 2.2. Stationary distributions for Markov chains with damping component -- 2.2.1. Stationary distributions for Markov chains with damping component -- 2.2.2. The stationary distribution of the Markov chain X0,n -- 2.3. A perturbation analysis for stationary distributions of Markov chains with damping component -- 2.3.1. Continuity property for stationary probabilities -- 2.3.2. Rate of convergence for stationary distributions -- 2.3.3. Asymptotic expansions for stationary distributions -- 2.3.4. Results of numerical experiments -- 2.4. Coupling and ergodic theorems for perturbed Markov chains with damping component -- 2.4.1. Coupling for regularly perturbed Markov chains with damping component -- 2.4.2. Coupling for singularly perturbed Markov chains with damping component -- 2.4.3. Ergodic theorems for perturbed Markov chains with damping component in the triangular array mode -- 2.4.4. Numerical examples -- 2.5. Acknowledgments -- 2.6. References -- 3 PageRank and Perturbed Markov Chains -- 3.1. Introduction -- 3.2. PageRank of the first-order perturbed Markov chain -- 3.3. PageRank of the second-order perturbed Markov chain.
• 3.4. Rates of convergence of PageRanks of first- and second-order perturbed Markov chains -- 3.5. Conclusion -- 3.6. Acknowledgments -- 3.7. References -- 4 Doubly Robust Data-driven Distributionally Robust Optimization -- 4.1. Introduction -- 4.2. DD-DRO, optimal transport and supervised machine learning -- 4.2.1. Optimal transport distances and discrepancies -- 4.3. Data-driven selection of optimal transport cost function -- 4.3.1. Data-driven cost functions via metric learning procedures -- 4.4. Robust optimization for metric learning -- 4.4.1. Robust optimization for relative metric learning -- 4.4.2. Robust optimization for absolute metric learning -- 4.5. Numerical experiments -- 4.6. Discussion and conclusion -- 4.7. References -- 5 A Comparison of Graph Centrality Measures Based on Lazy Random Walks -- 5.1. Introduction -- 5.1.1. Notations and abbreviations -- 5.1.2. Linear systems and the Neumann series -- 5.2. Review on some centrality measures -- 5.2.1. Degree centrality -- 5.2.2. Katz status and β-centralities -- 5.2.3. Eigenvector and cumulative nomination centralities -- 5.2.4. Alpha centrality -- 5.2.5. PageRank centrality -- 5.2.6. Summary of the centrality measures as steady state, shifted and power series -- 5.3. Generalizations of centrality measures -- 5.3.1. Priors to centrality measures -- 5.3.2. Lazy variants of centrality measures -- 5.3.3. Lazy α-centrality -- 5.3.4. Lazy Katz centrality -- 5.3.5. Lazy cumulative nomination centrality -- 5.4. Experimental results -- 5.5. Discussion -- 5.6. Conclusion -- 5.7. Acknowledgments -- 5.8. References -- 6 Error Detection in Sequential Laser Sensor Input -- 6.1. Introduction -- 6.2. Data description -- 6.3. Algorithms -- 6.3.1. Algorithm for consecutive changes in mean -- 6.3.2. Algorithm for burst detection -- 6.4. Results -- 6.5. Acknowledgments -- 6.6. References.
• 7 Diagnostics and Visualization of Point Process Models for Event Times on a Social Network -- 7.1. Introduction -- 7.2. Background -- 7.2.1. Univariate point processes -- 7.2.2. Network point processes -- 7.3. Model checking for time heterogeneity -- 7.3.1. Time rescaling theorem -- 7.3.2. Residual process -- 7.4. Model checking for network heterogeneity and structure -- 7.4.1. Kolmogorov-Smirnov test -- 7.4.2. Structure score based on the Pearson residual matrix -- 7.5. Summary -- 7.6. Acknowledgments -- 7.7. References -- PART 2: Data Analysis Methods and Tools -- 8 Exploring the Distribution of Conditional Quantile Estimates: An Application to Specific Costs of Pig Production in the European Union -- 8.1. Introduction -- 8.2. Conceptual framework and methodological aspects -- 8.2.1. The empirical model for estimating the specific production costs -- 8.2.2. The procedures for estimating and testing conditional quantiles -- 8.2.3. Symbolic PCA of the specific cost distributions -- 8.2.4. Symbolic clustering analysis of the specific cost distributions -- 8.3. Results -- 8.3.1. The SO-PCA of specific cost estimates -- 8.3.2. The divisive hierarchy of specific cost estimates -- 8.4. Conclusion -- 8.5. References -- 9 Maximization Problem Subject to Constraint of Availability in Semi-Markov Model of Operation -- 9.1. Introduction -- 9.2. Semi-Markov decision process -- 9.3. Semi-Markov decision model of operation -- 9.3.1. Description and assumptions -- 9.3.2. Model construction -- 9.4. Optimization problem -- 9.4.1. Linear programing method -- 9.5. Numerical example -- 9.6. Conclusion -- 9.7. References -- 10 The Impact of Multicollinearity on Big Data Multivariate Analysis Modeling -- 10.1. Introduction -- 10.2. Multicollinearity -- 10.3. Dimension reduction techniques -- 10.3.1. Beale et al. -- 10.3.2. Principal component analysis.
• 10.4. Application -- 10.4.1. The modeling of PPE -- 10.4.2. Concluding remarks -- 10.5. Acknowledgments -- 10.6. References -- 11 Weak Signals in High-Dimensional Poisson Regression Models -- 11.1. Introduction -- 11.2. Statistical background -- 11.3. Methodologies -- 11.3.1. Predictor screening methods -- 11.3.2. Post-screening parameter estimation methods -- 11.4. Numerical studies -- 11.4.1. Simulation settings and performance criteria -- 11.4.2. Results -- 11.5. Conclusion -- 11.6. Acknowledgments -- 11.7. References -- 12 Groundwater Level Forecasting for Water Resource Management -- 12.1. Introduction -- 12.2. Materials and methods -- 12.2.1. Study area -- 12.2.2. Forecast method -- 12.3. Results -- 12.4. Conclusion -- 12.5. References -- 13 Phase I Non-parametric Control Charts for Individual Observations: A Selective Review and Some Results -- 13.1. Introduction -- 13.1.1. Background -- 13.1.2. Univariate non-parametric process monitoring -- 13.2. Problem formulation -- 13.3. A comparative study -- 13.3.1. The existing methodologies -- 13.3.2. Simulation settings -- 13.3.3. Simulation-study results -- 13.4. Concluding remarks -- 13.5. References -- 14 On Divergence and Dissimilarity Measures for Multiple Time Series -- 14.1. Introduction -- 14.2. Classical measures -- 14.3. Divergence measures -- 14.4. Dissimilarity measures for ordered data -- 14.4.1. Standard dissimilarity measures -- 14.4.2. Advanced dissimilarity measures -- 14.5. Conclusion -- 14.6. References -- List of Authors -- Index -- Other titles from iSTE in Innovation, Entrepreneurship and Management -- EULA.
• Description based on print version record.