This volume presents recent methodological developments in Applied/Computational Statistics. A wide range of topics is covered including Advances on Statistical Computing Environments, Methods for Classification and Clustering, Computation for Graphical Models and Bayes Nets, Computational Econometrics, Computational Statistics and Data Mining, Statistics for Finance and Insurance, Information Retrieval for Text and Images, Knowledge Extraction by Models, Model Selection Algorithms, Models for Latent Class Detection, Multiple Testing Procedures, Random Search Algorithms, Robust Statistics and Signal Extraction and Filtering. Besides structural and theoretical results, the book presents a wide variety of applications in fields such as time-series analysis, econometrics, finance and insurance, biology, micro-array analysis, astronomy, text analysis and alcohol studies. The CD-ROM accompanying it also includes tutorial texts on Computational Finance and Writing R Packages. Combining new methodological advances with a wide variety of real applications, this volume is specially valuable for researchers and practitioners, providing new analytical tools useful in theoretical research and daily practice in computational statistics and applied statistics in general.