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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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , |
| Autor Corporativo: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Wiley-ISTE,
2019.
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| Edición: | 1st edition. |
| Temas: | |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
MARC
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| 520 | |a 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. | ||
| 542 | |f Copyright © 2019 by John Wiley & Sons |g 2019 | ||
| 550 | |a Made available through: Safari, an O'Reilly Media Company. | ||
| 588 | 0 | |a Online resource; Title from title page (viewed May 21, 2019). | |
| 505 | 0 | |a 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. | |
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