Data Analysis and Applications 1

Data Analysis and Applications 1

by Christos H. Skiadas, James R. Bozeman
Released May 2019
Publisher(s): Wiley-ISTE
ISBN: 9781786303820

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Book description

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.

Table of contents

  1. Cover
  2. Preface
  3. Introduction: 50 Years of Data Analysis: From Exploratory Data Analysis to Predictive Modeling and Machine Learning
    1. I.1. The revolt against mathematical statistics
    2. I.2. EDA and unsupervised methods for dimension reduction
    3. I.3. Predictive modeling
    4. I.4. Conclusion
    5. I.5. References
  4. PART 1: Clustering and Regression
    1. 1 Cluster Validation by Measurement of Clustering Characteristics Relevant to the User
      1. 1.1. Introduction
      2. 1.2. General notation
      3. 1.3. Aspects of cluster validity
      4. 1.4. Aggregation of indexes
      5. 1.5. Random clusterings for calibrating indexes
      6. 1.6. Examples
      7. 1.7. Conclusion
      8. 1.8. Acknowledgment
      9. 1.9. References
    2. 2 Histogram-Based Clustering of Sensor Network Data
      1. 2.1. Introduction
      2. 2.2. Time series data stream clustering
      3. 2.3. Results on real data
      4. 2.4. Conclusions
      5. 2.5. References
    3. 3 The Flexible Beta Regression Model
      1. 3.1. Introduction
      2. 3.2. The FB distribution
      3. 3.3. The FB regression model
      4. 3.4. Bayesian inference
      5. 3.5. Illustrative application
      6. 3.6. Conclusion
      7. 3.7. References
    4. 4 S-weighted Instrumental Variables
      1. 4.1. Summarizing the previous relevant results
      2. 4.2. The notations, framework, conditions and main tool
      3. 4.3. S-weighted estimator and its consistency
      4. 4.4. S-weighted instrumental variables and their consistency
      5. 4.5. Patterns of results of simulations
      6. 4.6. Acknowledgment
      7. 4.7. References
  5. PART 2: Models and Modeling
    1. 5 Grouping Property and Decomposition of Explained Variance in Linear Regression
      1. 5.1. Introduction
      2. 5.2. CAR scores
      3. 5.3. Variance decomposition methods and SVD
      4. 5.4. Grouping property of variance decomposition methods
      5. 5.5. Conclusions
      6. 5.6. References
    2. 6 On GARCH Models with Temporary Structural Changes
      1. 6.1. Introduction
      2. 6.2. The model
      3. 6.3. Identification
      4. 6.4. Simulation
      5. 6.5. Application
      6. 6.6. Concluding remarks
      7. 6.7. References
    3. 7 A Note on the Linear Approximation of TAR Models
      1. 7.1. Introduction
      2. 7.2. Linear representations and linear approximations of nonlinear models
      3. 7.3. Linear approximation of the TAR model
      4. 7.4. References
    4. 8 An Approximation of Social Well-Being Evaluation Using Structural Equation Modeling
      1. 8.1. Introduction
      2. 8.2. Wellness
      3. 8.3. Social welfare
      4. 8.4. Methodology
      5. 8.5. Results
      6. 8.6. Discussion
      7. 8.7. Conclusions
      8. 8.8. References
    5. 9 An SEM Approach to Modeling Housing Values
      1. 9.1. Introduction
      2. 9.2. Data
      3. 9.3. Analysis
      4. 9.4. Conclusions
      5. 9.5. References
    6. 10 Evaluation of Stopping Criteria for Ranks in Solving Linear Systems
      1. 10.1. Introduction
      2. 10.2. Methods
      3. 10.3. Formulation of linear systems
      4. 10.4. Stopping criteria
      5. 10.5. Numerical experimentation of stopping criteria
      6. 10.6. Conclusions
      7. 10.7. Acknowledgments
      8. 10.8. References
    7. 11 Estimation of a Two-Variable Second-Degree Polynomial via Sampling
      1. 11.1. Introduction
      2. 11.2. Proposed method
      3. 11.3. Experimental approaches
      4. 11.4. Conclusions
      5. 11.5. References
  6. PART 3: Estimators, Forecasting and Data Mining
    1. 12 Displaying Empirical Distributions of Conditional Quantile Estimates: An Application of Symbolic Data Analysis to the Cost Allocation Problem in Agriculture
      1. 12.1. Conceptual framework and methodological aspects of cost allocation
      2. 12.2. The empirical model of specific production cost estimates
      3. 12.3. The conditional quantile estimation
      4. 12.4. Symbolic analyses of the empirical distributions of specific costs
      5. 12.5. The visualization and the analysis of econometric results
      6. 12.6. Conclusion
      7. 12.7. Acknowledgments
      8. 12.8. References
    2. 13 Frost Prediction in Apple Orchards Based upon Time Series Models
      1. 13.1. Introduction
      2. 13.2. Weather database
      3. 13.3. ARIMA forecast model
      4. 13.4. Model building
      5. 13.5. Evaluation
      6. 13.6. ARIMA model selection
      7. 13.7. Conclusions
      8. 13.8. Acknowledgments
      9. 13.9. References
    3. 14 Efficiency Evaluation of Multiple-Choice Questions and Exams
      1. 14.1. Introduction
      2. 14.2. Exam efficiency evaluation
      3. 14.3. Real-life experiments and results
      4. 14.4. Conclusions
      5. 14.5. References
    4. 15 Methods of Modeling and Estimation in Mortality
      1. 15.1. Introduction
      2. 15.2. The appearance of life tables
      3. 15.3. On the law of mortality
      4. 15.4. Mortality and health
      5. 15.5. An advanced health state function form
      6. 15.6. Epilogue
      7. 15.7. References
    5. 16 An Application of Data Mining Methods to the Analysis of Bank Customer Profitability and Buying Behavior
      1. 16.1. Introduction
      2. 16.2. Data set
      3. 16.3. Short-term forecasting of customer profitability
      4. 16.4. Churn prediction
      5. 16.5. Next-product-to-buy
      6. 16.6. Conclusions and future research
      7. 16.7. References
  7. List of Authors
  8. Index
  9. End User License Agreement

Product information

  • Title: Data Analysis and Applications 1
  • Author(s): Christos H. Skiadas, James R. Bozeman
  • Release date: May 2019
  • Publisher(s): Wiley-ISTE
  • ISBN: 9781786303820