Data analysis and applications. 1, Clustering and regression, modeling-estimating, forecasting and data mining

✍ Scribed by Bozeman, James R.; Skiadas, Christos H (ed.)

• Publisher: Wiley; ISTE Ltd
• Year: 2019
• Tongue: English
• Leaves: 264
• Series: Innovation entrepreneurship and management series. Big data artificial intelligence and data analysis set ; 2
• Category: Library

✦ Table of Contents

Cover......Page 1
Half-Title Page......Page 2
Title Page......Page 3
Copyright Page......Page 4
Contents......Page 5
Preface......Page 11
Introduction: 50 Years of Data Analysis: From Exploratory Data Analysis to Predictive Modeling and Machine Learning......Page 14
I.1. The revolt against mathematical statistics......Page 15
I.2.1. The time of syntheses......Page 17
I.2.2. The time of clusterwise methods......Page 18
I.2.3. Extensions to new types of data......Page 19
I.2.4. Nonlinear data analysis......Page 20
I.2.5. The time of sparse methods......Page 21
I.3.1. Paradigms and paradoxes......Page 22
I.3.2. From statistical learning theory to empirical validation......Page 23
I.3.3. Challenges......Page 24
I.5. References......Page 25
PART 1: Clustering and Regression......Page 29
1.1. Introduction......Page 30
1.2. General notation......Page 32
1.3.1. Small within-cluster dissimilarities......Page 33
1.3.3. Representation of objects by centroids......Page 34
1.3.4. Representation of dissimilarity structure by clustering......Page 35
1.3.6. Density modes and valleys......Page 36
1.3.8. Entropy......Page 39
1.3.11. Stability......Page 40
1.4. Aggregation of indexes......Page 41
1.5. Random clusterings for calibrating indexes......Page 42
1.5.2. Stupid nearest neighbors clustering......Page 43
1.5.3. Calibration......Page 44
1.6.1. Artificial data set......Page 45
1.6.2. Tetragonula bees data......Page 47
1.7. Conclusion......Page 49
1.9. References......Page 50
2.1. Introduction......Page 52
2.2. Time series data stream clustering......Page 55
2.2.1. Local clustering of histogram data......Page 57
2.2.2. Online proximity matrix updating......Page 59
2.2.3. Off-line partitioning through the dynamic clustering algorithm for dissimilarity tables......Page 60
2.3. Results on real data......Page 61
2.5. References......Page 63
3.1. Introduction......Page 66
3.2.2. The FB distribution......Page 68
3.2.3. Reparameterization of the FB......Page 69
3.3. The FB regression model......Page 70
3.4. Bayesian inference......Page 71
3.5. Illustrative application......Page 74
3.6. Conclusion......Page 75
3.7. References......Page 77
4.1. Summarizing the previous relevant results......Page 80
4.2. The notations, framework, conditions and main tool......Page 82
4.3. S-weighted estimator and its consistency......Page 84
4.4. S-weighted instrumental variables and their consistency......Page 86
4.5. Patterns of results of simulations......Page 91
4.5.1. Generating the data......Page 92
4.5.2. Reporting the results......Page 93
4.7. References......Page 96
PART 2: Models and Modeling......Page 99
5.1. Introduction......Page 100
5.2.1. Definition and estimators......Page 101
5.3. Variance decomposition methods and SVD......Page 104
5.4. Grouping property of variance decomposition methods......Page 105
5.4.1. Analysis of grouping property for CAR scores......Page 106
5.4.2. Demonstration with two predictors......Page 107
5.4.3. Analysis of grouping property using SVD......Page 108
5.4.4. Application to the diabetes data set......Page 111
5.5. Conclusions......Page 112
5.6. References......Page 113
6.1. Introduction......Page 115
6.2.1. Trend model......Page 116
6.2.2. Intervention GARCH model......Page 117
6.4.1. Simulation on trend model......Page 120
6.5. Application......Page 122
6.6. Concluding remarks......Page 126
6.7. References......Page 127
7.1. Introduction......Page 128
7.2. Linear representations and linear approximations of nonlinear models......Page 130
7.3. Linear approximation of the TAR model......Page 132
7.4. References......Page 139
8.1. Introduction......Page 140
8.3. Social welfare......Page 141
8.4. Methodology......Page 142
8.5. Results......Page 143
8.8. References......Page 146
9.1. Introduction......Page 148
9.2. Data......Page 149
9.3. Analysis......Page 150
9.4. Conclusions......Page 157
9.5. References......Page 158
10.1. Introduction......Page 159
10.2.1. Preliminaries......Page 161
10.2.2. Iterative methods......Page 162
10.3. Formulation of linear systems......Page 164
10.4. Stopping criteria......Page 165
10.5. Numerical experimentation of stopping criteria......Page 168
10.5.2. Quantiles......Page 169
10.5.3. Kendall correlation coefficient as stopping criterion......Page 170
10.6. Conclusions......Page 172
10.8. References......Page 173
11.1. Introduction......Page 175
11.2.1. First restriction......Page 176
11.2.2. Second restriction......Page 177
11.2.4. Fourth restriction......Page 178
11.2.5. Fifth restriction......Page 179
11.2.6. Coefficient estimates......Page 180
11.3.1. Experiment A......Page 181
11.3.2. Experiment B......Page 183
11.5. References......Page 185
PART 3: Estimators, Forecasting and Data Mining......Page 186
12.1. Conceptual framework and methodological aspects of cost allocation......Page 187
12.2. The empirical model of specific production cost estimates......Page 188
12.3. The conditional quantile estimation......Page 189
12.4. Symbolic analyses of the empirical distributions of specific costs......Page 190
12.5. The visualization and the analysis of econometric results......Page 192
12.6. Conclusion......Page 198
12.8. References......Page 199
13.1. Introduction......Page 200
13.2. Weather database......Page 201
13.3. ARIMA forecast model......Page 202
13.3.1. Stationarity and differencing......Page 203
13.3.2. Non-seasonal ARIMA models......Page 205
13.4.1. ARIMA and LR models......Page 207
13.5. Evaluation......Page 208
13.6. ARIMA model selection......Page 209
13.7. Conclusions......Page 211
13.9. References......Page 212
14.1. Introduction......Page 214
14.2.1. Efficiency measures and efficiency weighted grades......Page 215
14.2.2. Iterative execution......Page 217
14.2.3. Postprocessing......Page 218
14.3. Real-life experiments and results......Page 219
14.4. Conclusions......Page 222
14.5. References......Page 223
15.1. Introduction......Page 224
15.2. The appearance of life tables......Page 225
15.3. On the law of mortality......Page 226
15.4. Mortality and health......Page 230
15.5. An advanced health state function form......Page 236
15.6. Epilogue......Page 239
15.7. References......Page 240
16.1. Introduction......Page 244
16.2. Data set......Page 246
16.3. Short-term forecasting of customer profitability......Page 249
16.4. Churn prediction......Page 254
16.5. Next-product-to-buy......Page 255
16.6. Conclusions and future research......Page 257
16.7. References......Page 258
List of Authors......Page 260
Index......Page 263

✦ Subjects

Data mining;Forecasting;Quantitative research;REFERENCE--Questions & Answers;Regression analysis;Electronic books;REFERENCE -- Questions & Answers