On the Role and Potential of High-Dimensional Biologic Data in Cancer Research -- Variable selection in regression - estimation, prediction,sparsity, inference -- Multivariate Nonparametric Regression -- Risk Estimation -- Tree-Based Methods -- Support Vector Machine Classification for High Dimensional Microarray Data Analysis, With Applications in Cancer Research -- Bayesian Approaches: Nonparametric Bayesian Analysis of Gene Expression Data.

With the advent of high-throughput technologies, various types of high-dimensional data have been generated in recent years for the understanding of biological processes, especially processes that relate to disease occurrence or management of cancer. Motivated by these important applications in cancer research, there has been a dramatic growth in the development of statistical methodology in the analysis of high-dimensional data, particularly related to regression model selection, estimation and prediction. High-Dimensional Data Analysis in Cancer Research, edited by Xiaochun Li and Ronghui Xu, is a collective effort to showcase statistical innovations for meeting the challenges and opportunities uniquely presented by the analytical needs of high-dimensional data in cancer research, particularly in genomics and proteomics. All the chapters included in this volume contain interesting case studies to demonstrate the analysis methodology. High-Dimensional Data Analysis in Cancer Research is an invaluable reference for researchers, statisticians, bioinformaticians, graduate students and data analysts working in the fields of cancer research.