{"product_id":"data-analysis-and-applications-1-clustering-and-regression-modeling-estimating-forecasting-and-data-mining-hardback-9781786303820","title":"Data Analysis and Applications 1; Clustering and Regression, Modeling-estimating, Forecasting and Data Mining (Hardback) 9781786303820","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eData Analysis and Applications 1\u003c\/font\u003e\u003cbr\u003e\r\n\u003cfont size=\"5\"\u003eClustering and Regression, Modeling-estimating, Forecasting and Data Mining\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\u003cp\u003e\u003cfont size=\"4\"\u003eChristos H. Skiadas (Edited by), C Skiadas (Author), James R. Bozeman (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781786303820, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 26 February 2019\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e288 pages\u003cbr\u003e23.4 x 15.8 x 2.3 cm, 0.612 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003eThis 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.\u003cbr\u003e\u003cbr\u003eVolume 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.\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003ePreface xi\u003c\/p\u003e \u003cp\u003eIntroduction xv\u003cbr\u003e\u003ci\u003eGilbert SAPORTA\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 1 Clustering and Regression \u003c\/b\u003e\u003cb\u003e1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 Cluster Validation by Measurement of Clustering Characteristics Relevant to the User \u003c\/b\u003e\u003cb\u003e3\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eChristian HENNIG\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e1.1 Introduction 3\u003c\/p\u003e \u003cp\u003e1.2 General notation 5\u003c\/p\u003e \u003cp\u003e1.3 Aspects of cluster validity 6\u003c\/p\u003e \u003cp\u003e1.3.1 Small within-cluster dissimilarities 6\u003c\/p\u003e \u003cp\u003e1.3.2 Between-cluster separation 7\u003c\/p\u003e \u003cp\u003e1.3.3 Representation of objects by centroids 7\u003c\/p\u003e \u003cp\u003e1.3.4 Representation of dissimilarity structure by clustering 8\u003c\/p\u003e \u003cp\u003e1.3.5 Small within-cluster gaps 9\u003c\/p\u003e \u003cp\u003e1.3.6 Density modes and valleys 9\u003c\/p\u003e \u003cp\u003e1.3.7 Uniform within-cluster density 12\u003c\/p\u003e \u003cp\u003e1.3.8 Entropy 12\u003c\/p\u003e \u003cp\u003e1.3.9 Parsimony 13\u003c\/p\u003e \u003cp\u003e1.3.10 Similarity to homogeneous distributional shapes 13\u003c\/p\u003e \u003cp\u003e1.3.11 Stability 13\u003c\/p\u003e \u003cp\u003e1.3.12 Further Aspects 14\u003c\/p\u003e \u003cp\u003e1.4 Aggregation of indexes 14\u003c\/p\u003e \u003cp\u003e1.5 Random clusterings for calibrating indexes 15\u003c\/p\u003e \u003cp\u003e1.5.1 Stupid K-centroids clustering 16\u003c\/p\u003e \u003cp\u003e1.5.2 Stupid nearest neighbors clustering 16\u003c\/p\u003e \u003cp\u003e1.5.3 Calibration 17\u003c\/p\u003e \u003cp\u003e1.6 Examples 18\u003c\/p\u003e \u003cp\u003e1.6.1 Artificial data set 18\u003c\/p\u003e \u003cp\u003e1.6.2 Tetragonula bees data 20\u003c\/p\u003e \u003cp\u003e1.7 Conclusion 22\u003c\/p\u003e \u003cp\u003e1.8 Acknowledgment 23\u003c\/p\u003e \u003cp\u003e1.9 References 23\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 Histogram-Based Clustering of Sensor Network Data \u003c\/b\u003e\u003cb\u003e25\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eAntonio BALZANELLA \u003c\/i\u003eand\u003ci\u003e Rosanna VERDE\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e2.1 Introduction 25\u003c\/p\u003e \u003cp\u003e2.2 Time series data stream clustering 28\u003c\/p\u003e \u003cp\u003e2.2.1 Local clustering of histogram data 30\u003c\/p\u003e \u003cp\u003e2.2.2 Online proximity matrix updating 32\u003c\/p\u003e \u003cp\u003e2.2.3 Off-line partitioning through the dynamic clustering algorithm for dissimilarity tables 33\u003c\/p\u003e \u003cp\u003e2.3 Results on real data 34\u003c\/p\u003e \u003cp\u003e2.4 Conclusions 36\u003c\/p\u003e \u003cp\u003e2.5 References 36\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 The Flexible Beta Regression Model \u003c\/b\u003e\u003cb\u003e39\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eSonia MIGLIORATI, Agnese MDI BRISCO \u003c\/i\u003eand\u003ci\u003e Andrea ONGARO\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e3.1 Introduction 39\u003c\/p\u003e \u003cp\u003e3.2 The FB distribution 41\u003c\/p\u003e \u003cp\u003e3.2.1 The beta distribution 41\u003c\/p\u003e \u003cp\u003e3.2.2 The FB distribution 41\u003c\/p\u003e \u003cp\u003e3.2.3 Reparameterization of the FB 42\u003c\/p\u003e \u003cp\u003e3.3 The FB regression model 43\u003c\/p\u003e \u003cp\u003e3.4 Bayesian inference 44\u003c\/p\u003e \u003cp\u003e3.5 Illustrative application 47\u003c\/p\u003e \u003cp\u003e3.6 Conclusion 48\u003c\/p\u003e \u003cp\u003e3.7 References 50\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 \u003ci\u003eS\u003c\/i\u003e-weighted Instrumental Variables \u003c\/b\u003e\u003cb\u003e53\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eJan Ámos VÍŠEK\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e4.1 Summarizing the previous relevant results 53\u003c\/p\u003e \u003cp\u003e4.2 The notations, framework, conditions and main tool 55\u003c\/p\u003e \u003cp\u003e4.3 \u003ci\u003eS\u003c\/i\u003e-weighted estimator and its consistency 57\u003c\/p\u003e \u003cp\u003e4.4 \u003ci\u003eS\u003c\/i\u003e-weighted instrumental variables and their consistency 59\u003c\/p\u003e \u003cp\u003e4.5 Patterns of results of simulations 64\u003c\/p\u003e \u003cp\u003e4.5.1 Generating the data 65\u003c\/p\u003e \u003cp\u003e4.5.2 Reporting the results 66\u003c\/p\u003e \u003cp\u003e4.6 Acknowledgment 69\u003c\/p\u003e \u003cp\u003e4.7 References 69\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 2 Models and Modeling \u003c\/b\u003e\u003cb\u003e73\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 Grouping Property and Decomposition of Explained Variance in Linear Regression \u003c\/b\u003e\u003cb\u003e75\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eHenri WALLARD\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e5.1 Introduction 75\u003c\/p\u003e \u003cp\u003e5.2 CAR scores 76\u003c\/p\u003e \u003cp\u003e5.2.1 Definition and estimators 76\u003c\/p\u003e \u003cp\u003e5.2.2 Historical criticism of the CAR scores 79\u003c\/p\u003e \u003cp\u003e5.3 Variance decomposition methods and SVD 79\u003c\/p\u003e \u003cp\u003e5.4 Grouping property of variance decomposition methods 80\u003c\/p\u003e \u003cp\u003e5.4.1 Analysis of grouping property for CAR scores 81\u003c\/p\u003e \u003cp\u003e5.4.2 Demonstration with two predictors 82\u003c\/p\u003e \u003cp\u003e5.4.3 Analysis of grouping property using SVD 83\u003c\/p\u003e \u003cp\u003e5.4.4 Application to the diabetes data set 86\u003c\/p\u003e \u003cp\u003e5.5 Conclusions 87\u003c\/p\u003e \u003cp\u003e5.6 References 88\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 On GARCH Models with Temporary Structural Changes \u003c\/b\u003e\u003cb\u003e91\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eNorio WATANABE \u003c\/i\u003eand \u003ci\u003eFumiaki OKIHARA\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e6.1 Introduction 91\u003c\/p\u003e \u003cp\u003e6.2 The model 92\u003c\/p\u003e \u003cp\u003e6.2.1 Trend model 92\u003c\/p\u003e \u003cp\u003e6.2.2 Intervention GARCH model 93\u003c\/p\u003e \u003cp\u003e6.3 Identification 96\u003c\/p\u003e \u003cp\u003e6.4 Simulation 96\u003c\/p\u003e \u003cp\u003e6.4.1 Simulation on trend model 96\u003c\/p\u003e \u003cp\u003e6.4.2 Simulation on intervention trend model 98\u003c\/p\u003e \u003cp\u003e6.5 Application 98\u003c\/p\u003e \u003cp\u003e6.6 Concluding remarks 102\u003c\/p\u003e \u003cp\u003e6.7 References 103\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 A Note on the Linear Approximation of TAR Models \u003c\/b\u003e\u003cb\u003e105\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eFrancesco GIORDANO, Marcella NIGLIO \u003c\/i\u003eand\u003ci\u003e Cosimo Damiano VITALE\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e7.1 Introduction 105\u003c\/p\u003e \u003cp\u003e7.2 Linear representations and linear approximations of nonlinear models 107\u003c\/p\u003e \u003cp\u003e7.3 Linear approximation of the TAR model 109\u003c\/p\u003e \u003cp\u003e7.4 References 116\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 An Approximation of Social Well-Being Evaluation Using Structural Equation Modeling \u003c\/b\u003e\u003cb\u003e117\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eLeonel SANTOS-BARRIOS, Monica RUIZ-TORRES, William GÓMEZ-DEMETRIO, Ernesto SÁNCHEZ-VERA, Ana LORGA DA SILVA \u003c\/i\u003eand\u003ci\u003e Francisco MARTÍNEZ-CASTAÑEDA\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e8.1 Introduction 117\u003c\/p\u003e \u003cp\u003e8.2 Wellness118\u003c\/p\u003e \u003cp\u003e8.3 Social welfare 118\u003c\/p\u003e \u003cp\u003e8.4 Methodology 119\u003c\/p\u003e \u003cp\u003e8.5 Results 120\u003c\/p\u003e \u003cp\u003e8.6 Discussion 123\u003c\/p\u003e \u003cp\u003e8.7 Conclusions 123\u003c\/p\u003e \u003cp\u003e8.8 References 123\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 An SEM Approach to Modeling Housing Values \u003c\/b\u003e\u003cb\u003e125\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eJim FREEMAN \u003c\/i\u003eand\u003ci\u003e Xin ZHAO\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e9.1 Introduction 125\u003c\/p\u003e \u003cp\u003e9.2 Data 126\u003c\/p\u003e \u003cp\u003e9.3 Analysis 127\u003c\/p\u003e \u003cp\u003e9.4 Conclusions 134\u003c\/p\u003e \u003cp\u003e9.5 References 135\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10 Evaluation of Stopping Criteria for Ranks in Solving Linear Systems \u003c\/b\u003e\u003cb\u003e137\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eBenard ABOLA, Pitos BIGANDA, Christopher ENGSTRÖM \u003c\/i\u003eand\u003ci\u003e Sergei SILVESTROV\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e10.1 Introduction 137\u003c\/p\u003e \u003cp\u003e10.2 Methods 139\u003c\/p\u003e \u003cp\u003e10.2.1 Preliminaries 139\u003c\/p\u003e \u003cp\u003e10.2.2 Iterative methods 140\u003c\/p\u003e \u003cp\u003e10.3 Formulation of linear systems 142\u003c\/p\u003e \u003cp\u003e10.4 Stopping criteria 143\u003c\/p\u003e \u003cp\u003e10.5 Numerical experimentation of stopping criteria 146\u003c\/p\u003e \u003cp\u003e10.5.1 Convergence of stopping criterion 147\u003c\/p\u003e \u003cp\u003e10.5.2 Quantiles 147\u003c\/p\u003e \u003cp\u003e10.5.3 Kendall correlation coefficient as stopping criterion 148\u003c\/p\u003e \u003cp\u003e10.6 Conclusions 150\u003c\/p\u003e \u003cp\u003e10.7 Acknowledgments 151\u003c\/p\u003e \u003cp\u003e10.8 References 151\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11 Estimation of a Two-Variable Second-Degree Polynomial via Sampling \u003c\/b\u003e\u003cb\u003e153\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eIoanna PAPATSOUMA, Nikolaos FARMAKIS \u003c\/i\u003eand \u003ci\u003eEleni KETZAKI\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e11.1 Introduction 153\u003c\/p\u003e \u003cp\u003e11.2 Proposed method 154\u003c\/p\u003e \u003cp\u003e11.2.1 First restriction 154\u003c\/p\u003e \u003cp\u003e11.2.2 Second restriction 155\u003c\/p\u003e \u003cp\u003e11.2.3 Third restriction 156\u003c\/p\u003e \u003cp\u003e11.2.4 Fourth restriction 156\u003c\/p\u003e \u003cp\u003e11.2.5 Fifth restriction 157\u003c\/p\u003e \u003cp\u003e11.2.6 Coefficient estimates 158\u003c\/p\u003e \u003cp\u003e11.3 Experimental approaches 159\u003c\/p\u003e \u003cp\u003e11.3.1 Experiment A 159\u003c\/p\u003e \u003cp\u003e11.3.2 Experiment B 161\u003c\/p\u003e \u003cp\u003e11.4 Conclusions 163\u003c\/p\u003e \u003cp\u003e11.5 References 163\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 3 Estimators, Forecasting and Data Mining \u003c\/b\u003e\u003cb\u003e165\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 12 Displaying Empirical Distributions of Conditional Quantile Estimates: An Application of Symbolic Data Analysis to the Cost Allocation Problem in Agriculture \u003c\/b\u003e\u003cb\u003e167\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eDominique DESBOIS\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e12.1 Conceptual framework and methodological aspects of cost allocation 167\u003c\/p\u003e \u003cp\u003e12.2 The empirical model of specific production cost estimates 168\u003c\/p\u003e \u003cp\u003e12.3 The conditional quantile estimation 169\u003c\/p\u003e \u003cp\u003e12.4 Symbolic analyses of the empirical distributions of specific costs 170\u003c\/p\u003e \u003cp\u003e12.5 The visualization and the analysis of econometric results 172\u003c\/p\u003e \u003cp\u003e12.6 Conclusion 178\u003c\/p\u003e \u003cp\u003e12.7 Acknowledgments 179\u003c\/p\u003e \u003cp\u003e12.8 References 179\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 13 Frost Prediction in Apple Orchards Based upon Time Series Models \u003c\/b\u003e\u003cb\u003e181\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eMonika ATOMKOWICZ \u003c\/i\u003eand\u003ci\u003e Armin OSCHMITT\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e13.1 Introduction 181\u003c\/p\u003e \u003cp\u003e13.2 Weather database 182\u003c\/p\u003e \u003cp\u003e13.3 ARIMA forecast model 183\u003c\/p\u003e \u003cp\u003e13.3.1 Stationarity and differencing 184\u003c\/p\u003e \u003cp\u003e13.3.2 Non-seasonal ARIMA models 186\u003c\/p\u003e \u003cp\u003e13.4 Model building 188\u003c\/p\u003e \u003cp\u003e13.4.1 ARIMA and LR models 188\u003c\/p\u003e \u003cp\u003e13.4.2 Binary classification of the frost data 189\u003c\/p\u003e \u003cp\u003e13.4.3 Training and test set 189\u003c\/p\u003e \u003cp\u003e13.5 Evaluation 189\u003c\/p\u003e \u003cp\u003e13.6 ARIMA model selection 190\u003c\/p\u003e \u003cp\u003e13.7 Conclusions 192\u003c\/p\u003e \u003cp\u003e13.8 Acknowledgments 193\u003c\/p\u003e \u003cp\u003e13.9 References 193\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 14 Efficiency Evaluation of Multiple-Choice Questions and Exams \u003c\/b\u003e\u003cb\u003e195\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eEvgeny GERSHIKOV \u003c\/i\u003eand\u003ci\u003e Samuel KOSOLAPOV\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e14.1 Introduction 195\u003c\/p\u003e \u003cp\u003e14.2 Exam efficiency evaluation 196\u003c\/p\u003e \u003cp\u003e14.2.1 Efficiency measures and efficiency weighted grades 196\u003c\/p\u003e \u003cp\u003e14.2.2 Iterative execution 198\u003c\/p\u003e \u003cp\u003e14.2.3 Postprocessing 199\u003c\/p\u003e \u003cp\u003e14.3 Real-life experiments and results 200\u003c\/p\u003e \u003cp\u003e14.4 Conclusions 203\u003c\/p\u003e \u003cp\u003e14.5 References 204\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 15 Methods of Modeling and Estimation in Mortality \u003c\/b\u003e\u003cb\u003e205\u003cbr\u003e\u003c\/b\u003e\u003ci\u003eChristos HSKIADAS \u003c\/i\u003eand\u003ci\u003e Konstantinos NZAFEIRIS\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e15.1 Introduction 205\u003c\/p\u003e \u003cp\u003e15.2 The appearance of life tables 206\u003c\/p\u003e \u003cp\u003e15.3 On the law of mortality 207\u003c\/p\u003e \u003cp\u003e15.4 Mortality and health 211\u003c\/p\u003e \u003cp\u003e15.5 An advanced health state function form 217\u003c\/p\u003e \u003cp\u003e15.6 Epilogue 220\u003c\/p\u003e \u003cp\u003e15.7 References 221\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 16 An Application of Data Mining Methods to the Analysis of Bank Customer Profitability and Buying Behavior \u003c\/b\u003e\u003cb\u003e225\u003cbr\u003e\u003c\/b\u003e\u003ci\u003ePedro GODINHO, Joana DIAS \u003c\/i\u003eand\u003ci\u003e Pedro TORRES\u003c\/i\u003e\u003c\/p\u003e \u003cp\u003e16.1 Introduction 225\u003c\/p\u003e \u003cp\u003e16.2 Data set 227\u003c\/p\u003e \u003cp\u003e16.3 Short-term forecasting of customer profitability 230\u003c\/p\u003e \u003cp\u003e16.4 Churn prediction 235\u003c\/p\u003e \u003cp\u003e16.5 Next-product-to-buy 236\u003c\/p\u003e \u003cp\u003e16.6 Conclusions and future research 238\u003c\/p\u003e \u003cp\u003e16.7 References 239\u003c\/p\u003e \u003cp\u003eList of Authors 241\u003c\/p\u003e \u003cp\u003eIndex 245\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Mathematics [\u003ca title=\"See our other books on Mathematics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Mathematics%20%5BPB%5D%22\"\u003ePB\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-ISTE","offers":[{"title":"Brand New","offer_id":52446639915288,"sku":"9781786303820","price":110.87,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0730\/2037\/5320\/files\/9781786303820.jpg?v=1785112211","url":"https:\/\/freshlyprintedbooks.co.uk\/products\/data-analysis-and-applications-1-clustering-and-regression-modeling-estimating-forecasting-and-data-mining-hardback-9781786303820","provider":"Freshly Printed Books","version":"1.0","type":"link"}