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Data Analysis and Applications 1: Clustering and Regression, Modeling-estimating, Forecasting and Data Mining

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Data Analysis and Applications 1: Clustering and Regression, Modeling-estimating, Forecasting and Data Mining

Christos H. Skiadas (Editor), James R. Bozeman (Editor)

ISBN: 978-1-119-59768-1 March 2019 Wiley-ISTE 286 Pages

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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.

Preface xi

Introduction xv
Gilbert SAPORTA

Part 1 Clustering and Regression 1

Chapter 1 Cluster Validation by Measurement of Clustering Characteristics Relevant to the User 3
Christian 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 25
Antonio 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 39
Sonia 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 53
Jan 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 75
Henri 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 91
Norio 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 105
Francesco 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 117
Leonel 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 125
Jim 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 137
Benard 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 153
Ioanna 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 167
Dominique 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 181
Monika 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 195
Evgeny 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 205
Christos 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 225
Pedro 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