Data analysis and applications : clustering and regression, modeling-estimating, forecasting and data mining / Christos H. Skiadas, James R. Bozeman.
By: Skiadas, Christos H
Language: English Publisher: Hoboken, NJ : ISTE Ltd/John Wiley and Sons Inc, 2018Description: 1 online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9781786303820Subject(s): Data miningGenre/Form: Electronic books.DDC classification: 001.42 Online resources: Full text available at Wiley Online Library Click here to view| Item type | Current location | Home library | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|---|
EBOOK
|
COLLEGE LIBRARY | COLLEGE LIBRARY | 001.42 Sk31 2018 (Browse shelf) | Available | CL-50596 |
About the Author
Christos H. Skiadas is the Founder and former Director of the Data Analysis and Forecasting Laboratory at the Technical University of Crete, Greece. He continues his work at the university at the ManLab in the Department of Production Engineering and Management.
James R. Bozeman holds a PhD in Mathematics from Dartmouth College, USA, and is Professor of Mathematics at the American University of Malta.
Table of contents
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
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.
There are no comments for this item.