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

Applied modeling techniques and data analysis 1, Computational data analysis methods and tools 1, Computational data analysis methods and tools /

Detalles Bibliográficos
Otros Autores: Karagrigoriou, Alex, editor (editor)
Formato: Libro electrónico
Idioma:Inglés
Publicado: London, England ; Hoboken, New Jersey : ISTE [2021]
Materias:
Quantitative research > Data processing.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009644256206719
Tabla de Contenidos:
  • 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.

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