Applied modeling techniques and data analysis. 1, Computational data analysis methods and tools / edited by Alex Karagrigoriou [and three others].

Format:

Book

Contributor:

Karagrigoriou, Alex, editor.

Language:

English

Subjects (All):

Quantitative research--Data processing.

Quantitative research.

Physical Description:

1 online resource (297 pages)

Place of Publication:

London, England ; Hoboken, New Jersey : ISTE : Wiley, [2021]

Summary:

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.

Contents:

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

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Notes:

Description based on print version record.

ISBN:

9781119821571

1119821576

9781119821588

1119821584

9781119821564

1119821568

OCLC:

1247656654