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Applied Modeling Techniques and Data Analysis 1

Release Date: 2021/05/01
ISBN: 9781786306739
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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

  1. Cover
  2. Title Page
  3. Copyright
  4. Preface
  5. PART 1: Computational Data Analysis
    1. 1 A Variant of Updating PageRank in Evolving Tree Graphs
    2. 1.1. Introduction
    3. 1.2. Notations and definitions
    4. 1.3. Updating the transition matrix
    5. 1.4. Updating the PageRank of a tree graph
    6. 1.5. Maintaining the levels of vertices in a changing tree graph
    7. 1.6. Conclusion
    8. 1.7. Acknowledgments
    9. 1.8. References
    10. 2 Nonlinearly Perturbed Markov Chains and Information Networks
    11. 2.1. Introduction
    12. 2.2. Stationary distributions for Markov chains with damping component
    13. 2.3. A perturbation analysis for stationary distributions of Markov chains with damping component
    14. 2.4. Coupling and ergodic theorems for perturbed Markov chains with damping component
    15. 2.5. Acknowledgments
    16. 2.6. References
    17. 3 PageRank and Perturbed Markov Chains
    18. 3.1. Introduction
    19. 3.2. PageRank of the first-order perturbed Markov chain
    20. 3.3. PageRank of the second-order perturbed Markov chain
    21. 3.4. Rates of convergence of PageRanks of first- and second-order perturbed Markov chains
    22. 3.5. Conclusion
    23. 3.6. Acknowledgments
    24. 3.7. References
    25. 4 Doubly Robust Data-driven Distributionally Robust Optimization
    26. 4.1. Introduction
    27. 4.2. DD-DRO, optimal transport and supervised machine learning
    28. 4.3. Data-driven selection of optimal transport cost function
    29. 4.4. Robust optimization for metric learning
    30. 4.5. Numerical experiments
    31. 4.6. Discussion and conclusion
    32. 4.7. References
    33. 5 A Comparison of Graph Centrality Measures Based on Lazy Random Walks
    34. 5.1. Introduction
    35. 5.2. Review on some centrality measures
    36. 5.3. Generalizations of centrality measures
    37. 5.4. Experimental results
    38. 5.5. Discussion
    39. 5.6. Conclusion
    40. 5.7. Acknowledgments
    41. 5.8. References
    42. 6 Error Detection in Sequential Laser Sensor Input
    43. 6.1. Introduction
    44. 6.2. Data description
    45. 6.3. Algorithms
    46. 6.4. Results
    47. 6.5. Acknowledgments
    48. 6.6. References
    49. 7 Diagnostics and Visualization of Point Process Models for Event Times on a Social Network
    50. 7.1. Introduction
    51. 7.2. Background
    52. 7.3. Model checking for time heterogeneity
    53. 7.4. Model checking for network heterogeneity and structure
    54. 7.5. Summary
    55. 7.6. Acknowledgments
    56. 7.7. References
  6. PART 2: Data Analysis Methods and Tools
    1. 8 Exploring the Distribution of Conditional Quantile Estimates: An Application to Specific Costs of Pig Production in the European Union
    2. 8.1. Introduction
    3. 8.2. Conceptual framework and methodological aspects
    4. 8.3. Results
    5. 8.4. Conclusion
    6. 8.5. References
    7. 9 Maximization Problem Subject to Constraint of Availability in Semi-Markov Model of Operation
    8. 9.1. Introduction
    9. 9.2. Semi-Markov decision process
    10. 9.3. Semi-Markov decision model of operation
    11. 9.4. Optimization problem
    12. 9.5. Numerical example
    13. 9.6. Conclusion
    14. 9.7. References
    15. 10 The Impact of Multicollinearity on Big Data Multivariate Analysis Modeling
    16. 10.1. Introduction
    17. 10.2. Multicollinearity
    18. 10.3. Dimension reduction techniques
    19. 10.4. Application
    20. 10.5. Acknowledgments
    21. 10.6. References
    22. 11 Weak Signals in High-Dimensional Poisson Regression Models
    23. 11.1. Introduction
    24. 11.2. Statistical background
    25. 11.3. Methodologies
    26. 11.4. Numerical studies
    27. 11.5. Conclusion
    28. 11.6. Acknowledgments
    29. 11.7. References
    30. 12 Groundwater Level Forecasting for Water Resource Management
    31. 12.1. Introduction
    32. 12.2. Materials and methods
    33. 12.3. Results
    34. 12.4. Conclusion
    35. 12.5. References
    36. 13 Phase I Non-parametric Control Charts for Individual Observations: A Selective Review and Some Results
    37. 13.1. Introduction
    38. 13.2. Problem formulation
    39. 13.3. A comparative study
    40. 13.4. Concluding remarks
    41. 13.5. References
    42. 14 On Divergence and Dissimilarity Measures for Multiple Time Series
    43. 14.1. Introduction
    44. 14.2. Classical measures
    45. 14.3. Divergence measures
    46. 14.4. Dissimilarity measures for ordered data
    47. 14.5. Conclusion
    48. 14.6. References
  7. List of Authors
  8. Index
  9. End User License Agreement
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