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Its goals are to 1 highlight and xpand the breadth of xisting methods in big data and high dimensional data analysis and their potential for the advancement of both the mathematical and statistical sciences; 2 identify important directions for future research in the theory of regularization methods in algorithmic development and in methodologies for different application areas; and 3 facilitate collaboration between theoretical and subject specific researcher.

This volume conveys some of the surprises puzzles and success stories in high dimensional and complex data analysis and related fields Its peer reviewed contributions showcase recent advances in variable selection stimation and prediction strategies for a host of useful models as well as ssential new developments in the fieldThe continued and rapid advancement of modern technology now allows scientists to collect data of increasingly unprecedented size and co.

Mplexity Examples include pigenomic data genomic data proteomic data high resolution image data high freuency financial data functional and longitudinal data and network data Simultaneous variable selection and stimation is one of the key statistical problems involved in analyzing such big and complex data The purpose of this book is to stimulate research and foster interaction between researchers in the area of high dimensional data analysis More concretely.

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