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Data Analysis and Applications 1

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 fiel...

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Bibliographic Details
Main Authors: Skiadas, Christos, Bozeman, James (Author)
Format: eBook
Language:English
Published: Wiley-ISTE 2019
Edition:1st edition
Subjects:
Data Mining / Fast
Data Mining
Forecasting / Fast
Regression Analysis / Http://id.loc.gov/authorities/subjects/sh85112392
Quantitative Research / Fast
Forecasting / Http://id.loc.gov/authorities/subjects/sh85050485
Regression Analysis / Fast
Forecasting
Data Mining / Http://id.loc.gov/authorities/subjects/sh97002073
Quantitative Research / Http://id.loc.gov/authorities/subjects/sh2007000909
Analyse De Régression
Regression Analysis
Prévision
Exploration De Données (informatique)
Recherche Quantitative
Online Access:
Collection: O'Reilly - Collection details see MPG.ReNa
Table of Contents:
  • 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
  • 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
  • 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
  • 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
  • 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 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
  • 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 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
  • 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

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