دانلود کتاب Data Analysis and Applications: Clustering and Regression, Modeling-estimating, Forecasting and Data Mining

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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