Data Analysis and Related Applications, Volume 5

Name: Data Analysis and Related Applications, Volume 5 | Models, Methods and Techniques
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Models, Methods and Techniques

Yiannis Dimotikalis Christos H. Skiadas(Herausgeber*in)

Wiley (Verlag)

1. Auflage

Erschienen am 7. August 2025

436 Seiten

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Inhalt

Cover
Title Page
Copyright Page
Contents
Chapter 1. Modeling/Forecasting Patient Recruitment in Multicenter Clinical Trials Using Time-dependent Models
1.1. Introduction
1.2. Poisson-gamma model with time-dependent rates
1.2.1. The case of homogeneous rates
1.3. Non-homogeneous PG model
1.3.1. Estimation at the interim stage
1.3.2. Simulation of non-homogeneous PG model
1.4. Testing the recruitment rates for homogeneity
1.4.1. Poisson-type test
1.4.2. Criterion for testing hypothesis H0
1.4.3. Poisson-gamma test
1.5. Implementations
1.6. Acknowledgment
1.7. References
Chapter 2. Forecasting the Next Megacycle of the Economy
2.1. Introduction
2.2. 2024: the end of an economic megacycle
2.3. The role of technology in shaping the future
2.4. The economic consequences of the new cycle
2.4.1. Presenting past megacycles
2.4.2. The structure of economic megacycles
2.4.3. Technology as a catalyst for megacycles
2.4.4. Historical patterns of energy and economic growth
2.5. The future of economic megacycles
2.5.1. The 2024-2080 megacycle: a new era of exponential change and growth
2.5.2. Stagnation phase: 2024-2052
2.5.3. Growth phase: 2052-2080
2.5.4. Geopolitical and societal implications
2.5.5. Role of labor and automation
2.6. Conclusions
2.7. References
Chapter 3. Modeling Functioning as a Determinant of Wellbeing: A Mediation Analysis
3.1. Introduction
3.2. Methods
3.2.1. Procedure and participants
3.2.2. Measures and item selection
3.2.3. Statistical analyses
3.3. Results
3.3.1. Univariate analysis
3.3.2. Bivariate analysis: correlation analysis
3.3.3. Multivariate analysis: mediation analysis
3.4. Conclusions
3.5. References
3.6. Appendix
Chapter 4. Cross-Cultural Issues in Psychological Assessment: A Multistrategy Approach
4.1. Introduction
4.2. Suicide risk among university students: India and Italy in direct comparison
4.3. Merging techniques: A better way in cross-cultural studies?
4.4. Do different people respond in the same way to common items?
4.5. Conclusions
4.6. References
Chapter 5. A Control Chart for Zero-Inflated Semi-Continuous Data
5.1. Introduction
5.1.1. Zero-inflated and hurdle models for count data
5.1.2. Inflated distributions for semi-continuous data
5.2. Zero-inflated Lomax distribution
5.2.1. The Lomax and the zero-inflated Lomax distributions
5.2.2. Maximum likelihood estimates
5.3. Shewhart control chart for monitoring zero-inflated Lomax data
5.4. Performance of the proposed control chart
5.5. Conclusions
5.6. Acknowledgments
5.7. References
Chapter 6. Further Results on Location Invariant Estimation of the Weibull Tail Coefficient
6.1. Introduction
6.2. Hill and GMs EVI and WTC-estimators
6.2.1. Power-mean-of-exponent-p (PMp) and Holder's mean-of-order-p (MOp = Hp) EVI estimation
6.2.2. WTC estimation
6.3. Classes of PORT-GMs (PGMs) WTC-estimators
6.4. Monte Carlo simulation of the PORT-GPMp (PGPMp) WTC-estimators
6.5. Overall comments and open research topics
6.6. Acknowledgments
6.7. References
Chapter 7. What Can we Learn from Malta? An Exploration of Gender Disparities in Education, Work and Money in Europe
7.1. Introduction
7.2. Gender gap: education, work and money
7.3. Gender Equality Index
7.4. Three-way data approach based on principal component analysis
7.5. Evidence from principal component analysis
7.6. Results of trajectory analysis
7.7. Conclusions
7.8. Acknowledgments
7.9. References
Chapter 8. Financial Analysis of a Public Hospital: The Case of the Corfu General Hospital
8.1. Introduction
8.2. Materials and methods
8.3. Results
8.3.1. Liquidity ratios
8.3.2. Financial structure and viability ratios
8.3.3. Activity ratios
8.3.4. Profitability ratios
8.4. Discussion
8.5. References
Chapter 9. EWMA Control Charts for Skewed Distributions
9.1. Introduction
9.2. Exponentially weighted moving average control charts
9.3. EMMA control charts for the non-normal process
9.3.1. The WVEWMA control chart
9.3.2. The WSDEWMA control chart
9.3.3. Newly proposed SCEWMA control chart
9.4. Real data
9.5. Simulation study
9.6. Simulation algorithm
9.7. Results and discussion
9.8. Conclusion
9.9. References
Chapter 10. Assessing the Impact of Renewable Energy Sources on Energy Economics: A Non-Linear Regression Analysis of Hellenic Energy Exchange Market Clearing Prices
10.1. Introduction
10.2. Methodology
10.2.1. Spearman's rank
10.2.2. Sparse autoencoder
10.3. Results
10.4. Discussion
10.5. Conclusions
10.6. Acknowledgments
10.7. References
Chapter 11. Enhancing Energy Market Stability: Comparative Analysis of Forecasting Techniques for Market Clearing Prices in the Day-Ahead Market
11.1. Introduction
11.2. Methodology
11.3. Results
11.4. Discussion
11.5. Conclusions
11.6. Acknowledgments
11.7. References
Chapter 12. Using the Coxian Continuous-Time Hidden Markov Model to Analyze Lombardy Region Wards for Older Individuals
12.1. Introduction
12.2. Methodology
12.3. Data and results
12.3.1. Data
12.3.2. Results
12.4. Conclusions
12.5. Practice implications
12.6. Conflict of interest
12.7. References
Chapter 13. Estimators for Extreme Value Index: Advancements in Tail Inference
13.1. Introduction
13.2. Estimators for the tail parameters
13.2.1. The new class of estimators for the EVI
13.2.2. Asymptotic properties of the GPWM estimators
13.2.3. Estimating an extreme quantile
13.3. Monte Carlo simulation study of the GPWM estimators
13.3.1. Methodology
13.3.2. Results
13.4. Conclusion
13.5. Acknowledgments
13.6. References
Chapter 14. Determinants of Students' Attitude Toward History: An Empirical Approach
14.1. Introduction
14.2. Previous research
14.2.1. Attitude toward history
14.2.2. Educational factors
14.2.3. Socioeconomic factors
14.3. Data and methods
14.3.1. Data
14.3.2. Empirical methodology
14.4. Results
14.5. Summary and conclusions
14.6. Appendices
14.6.1. Appendix A: the initial full questionnaire for the attitude survey toward history (EDIS)
14.6.2. Appendix B: the final questionnaire for the attitude survey toward history (EDIS)
14.6.3. Appendix C
14.7. References
Chapter 15. Methodological Procedures for Assessing the Quality of Death Certificates Due to Unknown Causes
15.1. Introduction
15.2. Methods
15.2.1. First step: correction of underregistration of deaths (f)
15.2.2. Second step: redistribution of deaths due to ill-defined causes
15.2.3. Third step: redistribution of deaths due to non-specific causes (garbage codes)
15.3. Illustrative example
15.4. Conclusions
15.5. References
Chapter 16. Health Status, Cancer and Pneumonia Death Rates in Europe: 2019-2022
16.1. Introduction
16.2. Background
16.3. Methods
16.4. Results and discussion
16.4. Conclusions
16.5. References
Chapter 17. A Bayesian Asymmetric Approach to Modeling Volatility on Portfolios with Many Assets
17.1. Introduction
17.2. Dynamic principal component analysis
17.3. Bayesian Student-t GJR(1,1) model
17.4. Asymmetric modeling of a portfolio with many assets
17.5. Forecasting, predictive ability and risk estimation
17.6. Conclusion
17.7. References
Chapter 18. Pandemic-Driven Innovations: Utilizing Online Learning and Big Data Analysis for Decision-Making in Educational Environments
18.1. Introduction
18.2. Literature review
18.2.1. Difficulties during the COVID-19 period
18.2.2. Effects of COVID-19 on education
18.2.3. Big data analysis in educational research
18.2.4. Related work
18.3. Methodology
18.4. Research questions
18.4.1. Dataset presentation
18.5. Conclusion
18.6. Suggestions for further research
18.7. References
Chapter 19. Credit Card Fraud Detection with Machine Learning and Big Data Analytics: A PySpark Framework Implementation
19.1. Introduction
19.2. Literature review
19.2.1. Introduction to credit card fraud detection
19.2.2. The importance of detecting credit card fraud
19.2.3. Role of machine learning in improving decision-making processes in fraud detection
19.2.4. Automated pattern recognition
19.2.5. Predictive modeling
19.2.6. Dynamic risk scoring
19.2.7. Anomaly detection
19.2.8. Natural language processing (NLP)
19.2.9. Integration with existing systems
19.2.10. Credit card fraud detection: machine learning applications
19.2.11. Credit card fraud detection using Apache Spark
19.2.12. How can machine learning algorithms enhance decision quality in detecting fraud?
19.2.13. Improved detection accuracy
19.2.14. Real-time processing and analysis
19.2.15. Handling big data and complex variables
19.2.16. Adaptive learning for evolving threats
19.2.17. Cost efficiency through automation
19.2.18. Enhanced scalability
19.3. Materials and methods
19.3.1. Performance evaluation
19.4. Results
19.4.1. Comparative analysis
19.5. Conclusions
19.6. Future work
19.7. Limitations
19.8. References
Chapter 20. Quantitative Modeling of the Demographic Aging Process
20.1. Introduction
20.2. Literature review
20.3. Methodology
20.3.1. Dependency ratios - double aging index
20.3.2. Multivariate regression model for the double aging index
20.4. Research results and discussion
20.5. Conclusions
20.6. References
Chapter 21. Hotel Sales During COVID-19: Evidence from the United States
21.1. Introduction
21.2. Literature review
21.3. Travel and tourism economic impact in 2022
21.3.1. Travel and tourism GDP
21.3.2. Travel and tourism employment
21.3.3. Travel and tourism forecasts
21.4. Key developments in the hospitality sector in 2022
21.4.1. Overall highlights
21.4.2. Hotel room demand
21.4.3. Occupancy
21.4.4. Room revenue
21.4.5. Workforce
21.4.6. State and local tax revenue
21.4.7. US hotel markets
21.5. Data and methodology
21.5.1. Data description
21.5.2. Methodology
21.5.3. LARC score
21.5.4. Graphical analysis of variables
21.6. Results
21.7. Concluding remarks and future research
21.8. Acknowledgments
21.9. References
Chapter 22. Investigating the Mediating Role of Religious Services Attendance in the Relationship Between Religion Variables and Social Class Perceptions
22.1. Introduction
22.2. Method
22.2.1. Procedure and participants
22.2.2. Measures
22.2.3. Statistical analyses
22.3. Results
22.3.1. Univariate analyses
22.3.2. Bivariate analyses: correlation analyses
22.3.3. Multivariate analyses: mediation analyses
22.4. Conclusions
22.5. References
Chapter 23. New Methods of Constructing Confidence Intervals of a Sensitive Proportion in Survey Statistics
23.1. Introduction
23.2. Method of moments (MM) for ICT data
23.3. EM estimation for ICT data and parametric bootstrap
23.3.1. Maximum likelihood via the EM algorithm
23.3.2. Percentile parametric bootstrap confidence intervals
23.4. "Almost exact" confidence intervals for ICT data
23.5. Simulation results
23.6. Conclusions
23.7. References
Chapter 24. Spreading Diseases Models Under Vaccination
24.1. Introduction
24.2. Modeling epidemic disease
24.3. Vaccination models analysis
24.4. SEIVR model
24.5. SIRV model
24.6. Conclusions
24.7. References
List of Authors
Index
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