Data Analysis and Applications 1 1st edition by Christos Skiadas ISBN 1119597684 9781119597681

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ISBN 10: 1119597684
ISBN 13: 9781119597681
Author: Christos Skiadas

This series of books collects a diverse array of work that provides the reader with theoretical and applied information on data analysis methods, models, and techniques, along with appropriate applications. Volume 1 begins with an introductory chapter by Gilbert Saporta, a leading expert in the field, who summarizes the developments in data analysis over the last 50 years. The book is then divided into three parts: Part 1 presents clustering and regression cases; Part 2 examines grouping and decomposition, GARCH and threshold models, structural equations, and SME modeling; and Part 3 presents symbolic data analysis, time series and multiple choice models, modeling in demography, and data mining.

Data Analysis and Applications 1 1st Table of contents:

Introduction: 50 Years of Data Analysis: From Exploratory Data Analysis to Predictive Modeling and Machine Learning

• I.1. The Revolt Against Mathematical Statistics
• I.2. EDA and Unsupervised Methods for Dimension Reduction
• I.3. Predictive Modeling
• I.4. Conclusion
• I.5. References

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PART 1: Clustering and Regression

1. Cluster Validation by Measurement of Clustering Characteristics Relevant to the User

• 1.1. Introduction
• 1.2. General Notation
• 1.3. Aspects of Cluster Validity
• 1.4. Aggregation of Indexes
• 1.5. Random Clusterings for Calibrating Indexes
• 1.6. Examples
• 1.7. Conclusion
• 1.8. Acknowledgment
• 1.9. References

2. Histogram-Based Clustering of Sensor Network Data

• 2.1. Introduction
• 2.2. Time Series Data Stream Clustering
• 2.3. Results on Real Data
• 2.4. Conclusions
• 2.5. References

3. The Flexible Beta Regression Model

• 3.1. Introduction
• 3.2. The FB Distribution
• 3.3. The FB Regression Model
• 3.4. Bayesian Inference
• 3.5. Illustrative Application
• 3.6. Conclusion
• 3.7. References

4. S-weighted Instrumental Variables

• 4.1. Summarizing the Previous Relevant Results
• 4.2. The Notations, Framework, Conditions and Main Tool
• 4.3. S-weighted Estimator and Its Consistency
• 4.4. S-weighted Instrumental Variables and Their Consistency
• 4.5. Patterns of Results of Simulations
• 4.6. Acknowledgment
• 4.7. References

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PART 2: Models and Modeling

5. Grouping Property and Decomposition of Explained Variance in Linear Regression

• 5.1. Introduction
• 5.2. CAR Scores
• 5.3. Variance Decomposition Methods and SVD
• 5.4. Grouping Property of Variance Decomposition Methods
• 5.5. Conclusions
• 5.6. References

6. On GARCH Models with Temporary Structural Changes

• 6.1. Introduction
• 6.2. The Model
• 6.3. Identification
• 6.4. Simulation
• 6.5. Application
• 6.6. Concluding Remarks
• 6.7. References

7. A Note on the Linear Approximation of TAR Models

• 7.1. Introduction
• 7.2. Linear Representations and Linear Approximations of Nonlinear Models
• 7.3. Linear Approximation of the TAR Model
• 7.4. References

8. An Approximation of Social Well-Being Evaluation Using Structural Equation Modeling

• 8.1. Introduction
• 8.2. Wellness
• 8.3. Social Welfare
• 8.4. Methodology
• 8.5. Results
• 8.6. Discussion
• 8.7. Conclusions
• 8.8. References

9. An SEM Approach to Modeling Housing Values

• 9.1. Introduction
• 9.2. Data
• 9.3. Analysis
• 9.4. Conclusions
• 9.5. References

10. Evaluation of Stopping Criteria for Ranks in Solving Linear Systems

• 10.1. Introduction
• 10.2. Methods
• 10.3. Formulation of Linear Systems
• 10.4. Stopping Criteria
• 10.5. Numerical Experimentation of Stopping Criteria
• 10.6. Conclusions
• 10.7. Acknowledgments
• 10.8. References

11. Estimation of a Two-Variable Second-Degree Polynomial via Sampling

• 11.1. Introduction
• 11.2. Proposed Method
• 11.3. Experimental Approaches
• 11.4. Conclusions
• 11.5. References

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PART 3: Estimators, Forecasting and Data Mining

12. Displaying Empirical Distributions of Conditional Quantile Estimates: An Application of Symbolic Data Analysis to the Cost Allocation Problem in Agriculture

• 12.1. Conceptual Framework and Methodological Aspects of Cost Allocation
• 12.2. The Empirical Model of Specific Production Cost Estimates
• 12.3. The Conditional Quantile Estimation
• 12.4. Symbolic Analyses of the Empirical Distributions of Specific Costs
• 12.5. The Visualization and the Analysis of Econometric Results
• 12.6. Conclusion
• 12.7. Acknowledgments
• 12.8. References

13. Frost Prediction in Apple Orchards Based Upon Time Series Models

• 13.1. Introduction
• 13.2. Weather Database
• 13.3. ARIMA Forecast Model
• 13.4. Model Building
• 13.5. Evaluation
• 13.6. ARIMA Model Selection
• 13.7. Conclusions
• 13.8. Acknowledgments
• 13.9. References

14. Efficiency Evaluation of Multiple-Choice Questions and Exams

• 14.1. Introduction
• 14.2. Exam Efficiency Evaluation
• 14.3. Real-Life Experiments and Results
• 14.4. Conclusions
• 14.5. References

15. Methods of Modeling and Estimation in Mortality

• 15.1. Introduction
• 15.2. The Appearance of Life Tables
• 15.3. On the Law of Mortality
• 15.4. Mortality and Health
• 15.5. An Advanced Health State Function Form
• 15.6. Epilogue
• 15.7. References

16. An Application of Data Mining Methods to the Analysis of Bank Customer Profitability and Buying Behavior

• 16.1. Introduction
• 16.2. Data Set
• 16.3. Short-Term Forecasting of Customer Profitability
• 16.4. Churn Prediction
• 16.5. Next-Product-to-Buy
• 16.6. Conclusions and Future Research
• 16.7. References

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