Table of Contents	5
	List of Contributors	8
	Part I Data Visualization	13
	Introduction	14
	Computational Statistics and Data Visualization	15
	The Chapters	17
	Outlook	23
	Principles	24
	Part II Principles	24
	A Brief History of Data Visualization	25
	Introduction	26
	Milestones Tour	27
	Statistical Historiography	52
	Final Thoughts	58
	References	59
	Good Graphics?	67
	Introduction	68
	Background	70
	Presentation ( What toWhom, How andWhy)	72
	Scientific Design Choices in Data Visualization	73
	Higher- dimensional Displays and Special Structures	80
	Practical Advice	86
	And Finally	87
	References	87
	Static Graphics	89
	Complete Plots	91
	Customization	94
	Extensibility	102
	Other Issues	108
	Summary	110
	References	110
	Data Visualization Through Their Graph Representations	112
	Introduction	113
	Data and Graphs	113
	Graph Layout Techniques	115
	Discussion and Concluding Remarks	127
	References	127
	Graph-theoretic Graphics	130
	Introduction	131
	Definitions	131
	Graph Drawing	133
	Geometric Graphs	145
	Graph-theoretic Analytics	152
	References	158
	High- dimensional Data Visualization	160
	Introduction	161
	Mosaic Plots	162
	Trellis Displays	166
	Parallel Coordinate Plots	173
	Projection Pursuit and the Grand Tour	181
	Recommendations	184
	References	186
	Multivariate Data Glyphs: Principles and Practice	188
	Introduction	189
	Data	189
	Mappings	190
	Examples of Existing Glyphs	191
	Biases in GlyphMappings	192
	Ordering of Data Dimensions/Variables	193
	Glyph Layout Options	197
	Evaluation	200
	Summary	204
	References	205
	Linked Views for Visual Exploration	208
	Visual Exploration by Linked Views	209
	Theoretical Structures for Linked Views	212
	Visualization Techniques for Linked Views	218
	Software	222
	Conclusion	223
	References	223
	Linked Data Views	225
	Motivation: Why Use Linked Views?	226
	The Linked Views Paradigm	229
	Brushing ScatterplotMatrices and Other Nonaggregated Views	232
	Generalizing to Aggregated Views	235
	Distance-based Linking	239
	Linking fromMultiple Views	240
	Linking to Domain-specific Views	243
	Summary	246
	Data Used in This Chapter	247
	References	248
	Visualizing Trees and Forests	250
	Introduction	251
	Individual Trees	251
	Visualizing Forests	263
	Conclusion	269
	References	271
	Methodologies	272
	Part III Methodologies	272
	Interactive Linked Micromap Plots for the Display of Geographically Referenced Statistical Data	273
	Introduction	274
	AMotivational Example	278
	Design Issues and Variations on StaticMicromaps	280
	Web-based Applications of LM Plots	282
	Constructing LM Plots	289
	Discussion	294
	References	297
	Grand Tours, Projection Pursuit Guided Tours, andManual Controls	301
	Introductory Notes	302
	Tours	307
	Using Tours with Numerical Methods	316
	End Notes	318
	References	318
	Multidimensional Scaling	321
	Proximity Data	322
	Metric MDS	325
	Non-metric MDS	328
	Example: Shakespeare Keywords	331
	Procrustes Analysis	336
	Unidimensional Scaling	337
	INDSCAL	339
	Correspondence Analysis and Reciprocal Averaging	344
	Large Data Sets and Other Numerical Approaches	347
	References	351
	Huge Multidimensional Data Visualization: Back to the Virtue of Principal Coordinates and Dendrograms in the New Computer Age	354
	Introduction	356
	The Geometric Approach to the Statistical Analysis	357
	Factorial Analysis	360
	Distance Visualization in	365
	Principal AxisMethods and Classification: aUnifiedView	370
	Computational Issues	371
	Factorial Plans and Dendrograms: the Challenge for Visualization	376
	An Application: the Survey of Italian Household Income andWealth	382
	Conclusion and Perspectives	388
	References	390
	Multivariate Visualization by Density Estimation	393
	Univariate Density Estimates	394
	Bivariate Density Estimates	405
	Higher- dimensional Density Estimates	410
	References	415
	Structured Sets of Graphs	418
	Introduction	420
	Cartesian Products and the Trellis Paradigm	420
	ScatterplotMatrices: splomandxysplom	422
	Regression Diagnostic Plots	432
	Analysis of Covariance Plots	434
	Interaction Plots	437
	Boxplots	442
	Graphical Display of Incidence and Relative Risk	445
	Summary	447
	File Name Conventions	447
	References	447
	Regression by Parts: Fitting Visually Interpretable Models with GUIDE	449
	Introduction	450
	Boston Housing Data – Effects of Collinearity	451
	Extension to GUIDE	455
	Mussels – Categorical Predictors and SIR	457
	Crash Tests – Outlier Detection Under Confounding	461
	Car Insurance Rates – Poisson Regression	467
	Conclusion	470
	References	471
	StructuralAdaptiveSmoothing by Propagation – Separation Methods	472
	Nonparametric Regression	473
	Structural Adaptation	476
	An Illustrative Univariate Example	479
	Examples and Applications	481
	Concluding Remarks	490
	References	492
	Smoothing Techniques for Visualisation	494
	Introduction	495
	Smoothing in One Dimension	497
	Smoothing in Two Dimensions	503
	Additive Models	508
	Discussion	512
	References	513
	Data Visualization via KernelMachines	540
	Introduction	541
	Kernel Machines in the Framework of an RKHS	542
	Kernel Principal Component Analysis	544
	Kernel Canonical Correlation Analysis	552
	Kernel Cluster Analysis	555
	References	559
	Visualizing Cluster Analysis and FiniteMixtureModels	561
	Introduction	562
	Hierarchical Cluster Analysis	564
	Partitioning Cluster Analysis	568
	Model-Based Clustering	580
	Summary	586
	References	586
	Visualizing Contingency Tables	588
	Introduction	589
	Two- Way Tables	590
	Using Colors for Residual-Based Shadings	597
	Selected Methods for Multiway Tables	605
	Conclusion	613
	References	613
	Mosaic Plots and Their Variants	616
	Definition and Construction	618
	Interpreting Mosaic Plots	621
	Variants	626
	RelatedWork and Generalization	634
	Implementations	639
	References	640
	Parallel Coordinates: Visualization, Exploration and Classification of High- Dimensional Data	642
	Introduction	643
	Exploratory Data Analysis with	647
	coords	647
	Classification	663
	Visual and Computational Models	667
	Parallel Coordinates: Quick Overview	670
	Future	675
	References	677
	Matrix Visualization	680
	Introduction	681
	RelatedWorks	681
	The Basic Principles of Matrix Visualization	682
	Generalization and Flexibility	689
	An Example	692
	Comparison with Other Graphical Techniques	696
	Matrix Visualization of Binary Data	699
	OtherModules and Extensions ofMV	703
	Conclusion	704
	References	705
	Visualization in Bayesian Data Analysis	708
	Introduction	709
	Using Visualization to Understand and Check Models	711
	Example: A HierarchicalModel of Structure in Social Networks	715
	Challenges Associated with the Graphical Display of Bayesian Inferences	721
	Summary	721
	References	722
	Programming Statistical Data Visualization in the Java Language	724
	Introduction	725
	Basics of Statistical Graphics Libraries and Java Programming	726
	Design and Implementation of a Java Graphics Library	734
	Concluding Remarks	752
	References	754
	Web-Based Statistical Graphics using XML Technologies	756
	Introduction	757
	XML-Based Vector Graphics Formats	758
	SVG	764
	X3D	770
	Applications	776
	References	787
	Selected Applications	789
	Part IV Selected Applications	789
	Visualization for Genetic Network Reconstruction	790
	Introduction	791
	Visualization for Data Preprocessing	791
	Visualization for Genetic Network Reconstruction	794
	References	806
	Reconstruction, Visualization and Analysis ofMedical Images	809
	Introduction	810
	PET Images	811
	Ultrasound Images	815
	Magnetic Resonance Images	818
	Conclusion and Discussion	822
	References	824
	Exploratory Graphics of a Financial Dataset	827
	Introduction	828
	Description of the Data	829
	First Graphics	830
	Outliers	833
	Scatterplots	837
	Mosaic Plots	839
	Initial Comparisons Between Bankrupt Companies	840
	Investigating Bigger Companies	844
	Summary	847
	Software	848
	References	848
	Graphical Data Representation in Bankruptcy Analysis	849
	Company RatingMethodology	850
	The SVM Approach	853
	Company Score Evaluation	856
	Variable Selection	856
	Conversion of Scores into PDs	861
	Colour Coding	863
	Conclusion	867
	References	867
	Visualizing Functional Data with an Application to eBay’s Online Auctions	869
	Introduction	870
	Online Auction Data fromeBay	872
	Visualization at the Object Recovery Stage	873
	Visualizing Functional Observations	878
	Interactive Information Visualization of Functional and Cross- sectional Information via TimeSearcher	886
	Further Challenges and Future Directions	892
	References	893
	Visualization Tools for Insurance Risk Processes	895
	Introduction	896
	Software	898
	Fitting Loss andWaiting Time Distributions	898
	Risk Process and its Visualization	908
	References	916
	Subject Index	917