Handbook of Data Visualization

Visualizing the data is an essential part of any data analysis. Modern computing developments have led to big improvements in graphic capabilities and there are many new possibilities for data displays. This book gives an overview of modern data visualization methods, both in theory and practice. It details modern graphical tools such as mosaic plots, parallel coordinate plots, and linked views. Coverage also examines graphical methodology for particular areas of statistics, for example Bayesian analysis, genomic data and cluster analysis, as well software for graphics.

Complete overview of data visualization Comprehensive coverage of the field, both in theory and practice Includes supplementary material: sn.pub/extras

Autorentext
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.

Inhalt
Data Visualization.- Principles.- A Brief History of Data Visualization.- Good Graphics?.- Static Graphics.- Data Visualization Through Their Graph Representations.- Graph-theoretic Graphics.- High-dimensional Data Visualization.- Multivariate Data Glyphs: Principles and Practice.- Linked Views for Visual Exploration.- Linked Data Views.- Visualizing Trees and Forests.- Methodologies.- Interactive Linked Micromap Plots for the Display of Geographically Referenced Statistical Data.- Grand Tours, Projection Pursuit Guided Tours, and Manual Controls.- Multidimensional Scaling.- Huge Multidimensional Data Visualization: Back to the Virtue of Principal Coordinates and Dendrograms in the New Computer Age.- Multivariate Visualization by Density Estimation.- Structured Sets of Graphs.- Regression by Parts: Fitting Visually Interpretable Models with GUIDE.- Structural Adaptive Smoothing by PropagationSeparation Methods.- Smoothing Techniques for Visualisation.- Data Visualization via Kernel Machines.- Visualizing Cluster Analysis and Finite Mixture Models.- Visualizing Contingency Tables.- Mosaic Plots and Their Variants.- Parallel Coordinates: Visualization, Exploration and Classification of High-Dimensional Data.- Matrix Visualization.- Visualization in Bayesian Data Analysis.- Programming Statistical Data Visualization in the Java Language.- Web-Based Statistical Graphics using XML Technologies.- Selected Applications.- Visualization for Genetic Network Reconstruction.- 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.