Preface xi Introduction xvGilbert SAPORTA Part 1 Clustering and Regression 1 Chapter 1 Cluster Validation by Measurement of Clustering Characteristics Relevant to the User 3Christian HENNIG 1.1 Introduction 3 1.2 General notation 5 1.3 Aspects of cluster validity 6 1.3.1 Small within-cluster dissimilarities 6 1.3.2 Between-cluster separation 7 1.3.3 Representation of objects by centroids 7 1.3.4 Representation of dissimilarity structure by clustering 8 1.3.5 Small within-cluster gaps 9 1.3.6 Density modes and valleys 9 1.3.7 Uniform within-cluster density 12 1.3.8 Entropy 12 1.3.9 Parsimony 13 1.3.10 Similarity to homogeneous distributional shapes 13 1.3.11 Stability 13 1.3.12 Further Aspects 14 1.4 Aggregation of indexes 14 1.5 Random clusterings for calibrating indexes 15 1.5.1 Stupid K-centroids clustering 16 1.5.2 Stupid nearest neighbors clustering 16 1.5.3 Calibration 17 1.6 Examples 18 1.6.1 Artificial data set 18 1.6.2 Tetragonula bees data 20 1.7 Conclusion 22 1.8 Acknowledgment 23 1.9 References 23 Chapter 2 Histogram-Based Clustering of Sensor Network Data 25Antonio BALZANELLA and Rosanna VERDE 2.1 Introduction 25 2.2 Time series data stream clustering 28 2.2.1 Local clustering of histogram data 30 2.2.2 Online proximity matrix updating 32 2.2.3 Off-line partitioning through the dynamic clustering algorithm for dissimilarity tables 33 2.3 Results on real data 34 2.4 Conclusions 36 2.5 References 36 Chapter 3 The Flexible Beta Regression Model 39Sonia MIGLIORATI, Agnese MDI BRISCO and Andrea ONGARO 3.1 Introduction 39 3.2 The FB distribution 41 3.2.1 The beta distribution 41 3.2.2 The FB distribution 41 3.2.3 Reparameterization of the FB 42 3.3 The FB regression model 43 3.4 Bayesian inference 44 3.5 Illustrative application 47 3.6 Conclusion 48 3.7 References 50 Chapter 4 S-weighted Instrumental Variables 53Jan Ámos VÍŠEK 4.1 Summarizing the previous relevant results 53 4.2 The notations, framework, conditions and main tool 55 4.3 S-weighted estimator and its consistency 57 4.4 S-weighted instrumental variables and their consistency 59 4.5 Patterns of results of simulations 64 4.5.1 Generating the data 65 4.5.2 Reporting the results 66 4.6 Acknowledgment 69 4.7 References 69 Part 2 Models and Modeling 73 Chapter 5 Grouping Property and Decomposition of Explained Variance in Linear Regression 75Henri WALLARD 5.1 Introduction 75 5.2 CAR scores 76 5.2.1 Definition and estimators 76 5.2.2 Historical criticism of the CAR scores 79 5.3 Variance decomposition methods and SVD 79 5.4 Grouping property of variance decomposition methods 80 5.4.1 Analysis of grouping property for CAR scores 81 5.4.2 Demonstration with two predictors 82 5.4.3 Analysis of grouping property using SVD 83 5.4.4 Application to the diabetes data set 86 5.5 Conclusions 87 5.6 References 88 Chapter 6 On GARCH Models with Temporary Structural Changes 91Norio WATANABE and Fumiaki OKIHARA 6.1 Introduction 91 6.2 The model 92 6.2.1 Trend model 92 6.2.2 Intervention GARCH model 93 6.3 Identification 96 6.4 Simulation 96 6.4.1 Simulation on trend model 96 6.4.2 Simulation on intervention trend model 98 6.5 Application 98 6.6 Concluding remarks 102 6.7 References 103 Chapter 7 A Note on the Linear Approximation of TAR Models 105Francesco GIORDANO, Marcella NIGLIO and Cosimo Damiano VITALE 7.1 Introduction 105 7.2 Linear representations and linear approximations of nonlinear models 107 7.3 Linear approximation of the TAR model 109 7.4 References 116 Chapter 8 An Approximation of Social Well-Being Evaluation Using Structural Equation Modeling 117Leonel SANTOS-BARRIOS, Monica RUIZ-TORRES, William GÓMEZ-DEMETRIO, Ernesto SÁNCHEZ-VERA, Ana LORGA DA SILVA and Francisco MARTÍNEZ-CASTAÑEDA 8.1 Introduction 117 8.2 Wellness118 8.3 Social welfare 118 8.4 Methodology 119 8.5 Results 120 8.6 Discussion 123 8.7 Conclusions 123 8.8 References 123 Chapter 9 An SEM Approach to Modeling Housing Values 125Jim FREEMAN and Xin ZHAO 9.1 Introduction 125 9.2 Data 126 9.3 Analysis 127 9.4 Conclusions 134 9.5 References 135 Chapter 10 Evaluation of Stopping Criteria for Ranks in Solving Linear Systems 137Benard ABOLA, Pitos BIGANDA, Christopher ENGSTRÖM and Sergei SILVESTROV 10.1 Introduction 137 10.2 Methods 139 10.2.1 Preliminaries 139 10.2.2 Iterative methods 140 10.3 Formulation of linear systems 142 10.4 Stopping criteria 143 10.5 Numerical experimentation of stopping criteria 146 10.5.1 Convergence of stopping criterion 147 10.5.2 Quantiles 147 10.5.3 Kendall correlation coefficient as stopping criterion 148 10.6 Conclusions 150 10.7 Acknowledgments 151 10.8 References 151 Chapter 11 Estimation of a Two-Variable Second-Degree Polynomial via Sampling 153Ioanna PAPATSOUMA, Nikolaos FARMAKIS and Eleni KETZAKI 11.1 Introduction 153 11.2 Proposed method 154 11.2.1 First restriction 154 11.2.2 Second restriction 155 11.2.3 Third restriction 156 11.2.4 Fourth restriction 156 11.2.5 Fifth restriction 157 11.2.6 Coefficient estimates 158 11.3 Experimental approaches 159 11.3.1 Experiment A 159 11.3.2 Experiment B 161 11.4 Conclusions 163 11.5 References 163 Part 3 Estimators, Forecasting and Data Mining 165 Chapter 12 Displaying Empirical Distributions of Conditional Quantile Estimates: An Application of Symbolic Data Analysis to the Cost Allocation Problem in Agriculture 167Dominique DESBOIS 12.1 Conceptual framework and methodological aspects of cost allocation 167 12.2 The empirical model of specific production cost estimates 168 12.3 The conditional quantile estimation 169 12.4 Symbolic analyses of the empirical distributions of specific costs 170 12.5 The visualization and the analysis of econometric results 172 12.6 Conclusion 178 12.7 Acknowledgments 179 12.8 References 179 Chapter 13 Frost Prediction in Apple Orchards Based upon Time Series Models 181Monika ATOMKOWICZ and Armin OSCHMITT 13.1 Introduction 181 13.2 Weather database 182 13.3 ARIMA forecast model 183 13.3.1 Stationarity and differencing 184 13.3.2 Non-seasonal ARIMA models 186 13.4 Model building 188 13.4.1 ARIMA and LR models 188 13.4.2 Binary classification of the frost data 189 13.4.3 Training and test set 189 13.5 Evaluation 189 13.6 ARIMA model selection 190 13.7 Conclusions 192 13.8 Acknowledgments 193 13.9 References 193 Chapter 14 Efficiency Evaluation of Multiple-Choice Questions and Exams 195Evgeny GERSHIKOV and Samuel KOSOLAPOV 14.1 Introduction 195 14.2 Exam efficiency evaluation 196 14.2.1 Efficiency measures and efficiency weighted grades 196 14.2.2 Iterative execution 198 14.2.3 Postprocessing 199 14.3 Real-life experiments and results 200 14.4 Conclusions 203 14.5 References 204 Chapter 15 Methods of Modeling and Estimation in Mortality 205Christos HSKIADAS and Konstantinos NZAFEIRIS 15.1 Introduction 205 15.2 The appearance of life tables 206 15.3 On the law of mortality 207 15.4 Mortality and health 211 15.5 An advanced health state function form 217 15.6 Epilogue 220 15.7 References 221 Chapter 16 An Application of Data Mining Methods to the Analysis of Bank Customer Profitability and Buying Behavior 225Pedro GODINHO, Joana DIAS and Pedro TORRES 16.1 Introduction 225 16.2 Data set 227 16.3 Short-term forecasting of customer profitability 230 16.4 Churn prediction 235 16.5 Next-product-to-buy 236 16.6 Conclusions and future research 238 16.7 References 239 List of Authors 241 Index 245

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.