Statistical Analysis for High-Dimensional Data: The Abel Symposium 2014

Some Themes in High-Dimensional Statistics: A. Frigessi et al.- LaplaceAppoximation in High-Dimensional Bayesian Regression: R. Barber, M. Drton etal.- Preselection in Lasso-Type Analysis for Ultra-High Dimensional GenomicExploration: L.C. Bergersen, I. Glad et al.- Spectral Clustering and Block Models:a Review and a new Algorithm: S. Bhattacharyya et al.- Bayesian HierarchicalMixture Models: L. Bottelo et al.- iBATCGH; Integrative Bayesian Analysis of Transcriptomicand CGH Data: Cassese, M. Vannucci et al.- Models of Random SparseEigenmatrices and Bayesian Analysis of Multivariate Structure: A.J. Cron, M. West.-Combining Single and Paired End RNA-seq Data for Differential Expression Analysis:F. Feng, T.Speed et al.- An Imputation Method for Estimation the Learning Curvein Classification Problems: E. Laber et al.- Baysian Feature Allocation Modelsfor Tumor Heterogeneity: J. Lee, P. Mueller et al.- Bayesian Penalty Mixing:The Case of a Non-Separable Penalty: V. Rockova et al.- Confidence Intervalsfor Maximin Effects in Inhomogeneous Large Scale Data: D. Rothenhausler et al.-Chisquare Confidence Sets in High-Dimensional Regression: S. van de Geer et al.