Data Analysis and Applications 1

Name: Data Analysis and Applications 1 | Clustering and Regression, Modeling-estimating, Forecasting and Data Mining
Brand: Polity
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Clustering and Regression, Modeling-estimating, Forecasting and Data Mining

Christos H. Skiadas James R. Bozeman(Herausgeber*in)

Polity (Verlag)

1. Auflage

Erschienen am 4. März 2019

288 Seiten

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Inhalt

Cover
Half-Title Page
Title Page
Copyright Page
Contents
Preface
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.2.1. The time of syntheses
I.2.2. The time of clusterwise methods
I.2.3. Extensions to new types of data
I.2.4. Nonlinear data analysis
I.2.5. The time of sparse methods
I.3. Predictive modeling
I.3.1. Paradigms and paradoxes
I.3.2. From statistical learning theory to empirical validation
I.3.3. Challenges
I.4. Conclusion
I.5. References
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
1.3.1. Small within-cluster dissimilarities
1.3.2. Between-cluster separation
1.3.3. Representation of objects by centroids
1.3.4. Representation of dissimilarity structure by clustering
1.3.5. Small within-cluster gaps
1.3.6. Density modes and valleys
1.3.7. Uniform within-cluster density
1.3.8. Entropy
1.3.9. Parsimony
1.3.10. Similarity to homogeneous distributional shapes
1.3.11. Stability
1.3.12. Further Aspects
1.4. Aggregation of indexes
1.5. Random clusterings for calibrating indexes
1.5.1. Stupid K-centroids clustering
1.5.2. Stupid nearest neighbors clustering
1.5.3. Calibration
1.6. Examples
1.6.1. Artificial data set
1.6.2. Tetragonula bees data
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
2.2.1. Local clustering of histogram data
2.2.2. Online proximity matrix updating
2.2.3. Off-line partitioning through the dynamic clustering algorithm for dissimilarity tables
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
3.2.1. The beta distribution
3.2.2. The FB distribution
3.2.3. Reparameterization of the FB
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
4.5.1. Generating the data
4.5.2. Reporting the results
4.6. Acknowledgment
4.7. References
PART 2: Models and Modeling
5. Grouping Property and Decomposition of Explained Variance in Linear Regression
5.1. Introduction
5.2. CAR scores
5.2.1. Definition and estimators
5.2.2. Historical criticism of the CAR scores
5.3. Variance decomposition methods and SVD
5.4. Grouping property of variance decomposition methods
5.4.1. Analysis of grouping property for CAR scores
5.4.2. Demonstration with two predictors
5.4.3. Analysis of grouping property using SVD
5.4.4. Application to the diabetes data set
5.5. Conclusions
5.6. References
6. On GARCH Models with Temporary Structural Changes
6.1. Introduction
6.2. The model
6.2.1. Trend model
6.2.2. Intervention GARCH model
6.3. Identification
6.4. Simulation
6.4.1. Simulation on trend model
6.4.2. Simulation on intervention trend model
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
10.2.1. Preliminaries
10.2.2. Iterative methods
10.3. Formulation of linear systems
10.4. Stopping criteria
10.5. Numerical experimentation of stopping criteria
10.5.1. Convergence of stopping criterion
10.5.2. Quantiles
10.5.3. Kendall correlation coefficient as stopping criterion
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
11.2.1. First restriction
11.2.2. Second restriction
11.2.3. Third restriction
11.2.4. Fourth restriction
11.2.5. Fifth restriction
11.2.6. Coefficient estimates
11.3. Experimental approaches
11.3.1. Experiment A
11.3.2. Experiment B
11.4. Conclusions
11.5. References
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
13.3.1. Stationarity and differencing
13.3.2. Non-seasonal ARIMA models
13.4. Model building
13.4.1. ARIMA and LR models
13.4.2. Binary classification of the frost data
13.4.3. Training and test set
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
14.2.1. Efficiency measures and efficiency weighted grades
14.2.2. Iterative execution
14.2.3. Postprocessing
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
List of Authors
Index
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