Handbook of Data Visualization

I. Data Visualization

II. Principles: A Brief History of Data Visualization
- Good Graphics
- Static Graphics
- Data Visualization Through Their Graph Representations
- High Dimensional Data Visualization
- Multivariate Data Glyphs: Principles and Practice
- Graph-Theoretic Graphics
- Linked Data Views
- Linked views for visual exploration
- Visualizing Trees and Forests

III. Methodologies: Interactive Linked Micromap Plots for the Display of Geographically Referenced Statistical Data
- Grand Tours, Projection Pursuit Guided Tours and Manual Controls
- Multidimensional Scaling - A Review
- Huge multidimensional data visualization: back to the virtue of principal coordinates and dendrograms in the new computer age
- Multivariate Density Estimation for Visualization
- Structured Sets of Graphs
- Structural adaptive smoothing by Propagation-Separation-methods
- Smoothing Techniques for Visualization
- Data Visualization via Kernel Machines
- Visualization of Cluster Analysis and Finite Mixture Models
- Visualizing Contingency Tables
- Mosaicplots and their Variations
- Parallel Coordinates: Visualization and Classification of High Dimensional Datasets
- Matrix Visualization
- Visualization in Bayesian Analysis
- Java Tools and Environment for Statistical Data Visualization
- Web-based Statistical Graphics using XML Technologies
- Regression by Parts: Fitting Visually Interpretable Models with GUIDE

IV. Selected Applications: Data Visualization for Genetic Networks Reconstruction
- Visualizing Genomic Data
- Reconstruction, Visualization and Analysis of Medical Images
- Exploratory Graphics of a Financial Dataset
- Graphical Data Representation in Bankruptcy Analysis
- Visualizing Functional Data With an Application to eBay's Online Auctions
- Visualization Tools for Insurance Risk Processes.

Wolfgang Härdle is a professor of statistics at the Humboldt-Universität zu Berlin and director of C.A.S.E. the Centre for Applied Statistics and Economics. He teaches quantitative finance and semiparametric statistical methods. His research focuses on dynamic factor models, multivariate statistics in finance and computational statistics. He is an elected ISI member and advisor to the Guanghua School of Management, Peking University and to National Central University, Taiwan.

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