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Data Analysis and Applications 1

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This series of books collects a diverse array of work that provides the reader with theoretical and applied information on data an... Weiterlesen
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Beschreibung

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.

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.

Autorentext
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.

Inhalt

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 VÍEK

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...

Produktinformationen

Titel: Data Analysis and Applications 1
Untertitel: Clustering and Regression, Modeling-estimating, Forecasting and Data Mining
Editor:
EAN: 9781119597575
Digitaler Kopierschutz: Adobe-DRM
Format: E-Book (pdf)
Hersteller: Wiley-ISTE
Genre: Wahrscheinlichkeitstheorie, Stochastik, Mathematische Statistik
Anzahl Seiten: 286
Veröffentlichung: 04.03.2019
Dateigrösse: 7.8 MB