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This book features research contributions from The Abel Symposium on Statistical Analysis for High Dimensional Data, held in Nyvågar, Lofoten, Norway, in May 2014. The focus of the symposium was on statistical and machine learning methodologies specifically developed for inference in “big data” situations, with particular reference to genomic applications. The contributors, who are among the most prominent researchers on the theory of statistics for high dimensional inference, present new theories and methods, as well as challenging applications and computational solutions. Specific themes include, among others, variable selection and screening, penalised regression, sparsity, thresholding, low dimensional structures, computational challenges, non-convex situations, learning graphical models, sparse covariance and precision matrices, semi- and non-parametric formulations, multiple testing, classification, factor models, clustering, and preselection. Highlighting cutting-edge research and casting light on future research directions, the contributions will benefit graduate students and researchers in computational biology, statistics and the machine learning community.

Mathematics - General --- Mathematics --- Physical Sciences & Mathematics --- Mathematical statistics. --- Abel Symposium. --- Statistical inference --- Statistics, Mathematical --- Statistical methods --- Mathematics. --- Bioinformatics. --- Computer mathematics. --- Statistics. --- Computational Mathematics and Numerical Analysis. --- Statistical Theory and Methods. --- Statistics and Computing/Statistics Programs. --- Statistics for Life Sciences, Medicine, Health Sciences. --- Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. --- Statistical analysis --- Statistical data --- Statistical science --- Econometrics --- Computer mathematics --- Discrete mathematics --- Electronic data processing --- Bio-informatics --- Biological informatics --- Biology --- Information science --- Computational biology --- Systems biology --- Math --- Science --- Data processing --- Statistics --- Probabilities --- Sampling (Statistics) --- Computer science --- Statistics .